Social
Seoul, Korea
Conference
ICML 2026
July 6 - 11, 2026
Seoul, South Korea

Overview

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning.

Sponsorship Details

Diamond
Booth B207
Conference website
Related content

Accepted publications

Booth Schedule

Presentations:

11:30 - 12:00 PM Talk: From offline evidence to online action: Decision-making under imperfect evaluation - Yuhao Wang

12:00 - 12:30 PM Talk: Building a foundational customer model: LLM fine-tuning for enterprise sales recommendations - Jonathan Taws, Laurent Mombaerts

12:30 - 1:00 PM Talk: Cross-border fulfillment center selection via doubly robust estimation and linear programming - Kriti Mahajan

1:30 - 2:00 PM Talk: Physical AI on AWS: Enabling robots to see, plan, and act in the real world - Jack Cho, Minsoo Khang, Sanggyu Biern, Tatsuo Azeyanagi, Xiaogang Wang

2:00- 2:30 PM Talk: XB e-commerce selection benchmarking model - Kriti Mahajan

2:30 - 3:00 PM Talk: Amazon bio discovery: An agentic lab-in-the-loop platform for accelerated drug design - Jiwon Kim

3:00 - 3:30 PM Talk: Human-centered edge AI: From robotics to AR glasses - Yelin Kim

3:30 - 3:45 PM Talk: Accelerate research with AI agent on AWS - Neung-Sun Shin

3:45 - 4:00 PM Talk: Multi-node GPU clustering LLM training on AWS 101 - Daekeun Kim

Chat with scientists:
11:00 - 12:00 PM

Team

Domain/research

Amazonian

Prime Video (London)

Computer Vision, AV Foundational Models, VLMs

Benoit Vallade

Seller Partner Services (Seattle)

NLP, ML, Deep Learning, Reasoning, Evaluation Systems

Rutu Mulkar

AGI Info for AWS (Tel Aviv)

Vision-Language Models, Document Understanding, Discrete Diffusion Models, Agentic Systems

Niv Nayman

1:30 - 2:30 PM

Team

Domain/research

Amazonian

AWS MPS Science (Seoul)

Large Language Models, Agentic AI

Sina Amini Niaki

AGI Autonomy (SF)

LLM, Computer Use Agents

Hyungjun Lee

Amazon Robotics (Boston)

Computer Vision, Robotics

ByeongUk Lee

AWS MPS Science (Austin)

Reliability on Generative Outputs, Uncertainty Quantification for LLMs and Agents, Conformal Prediction, Hallucination Mitigation for Generative Outputs

Kris Pan

2:30 - 3:30 PM

Team

Domain/research

Amazonian

Ads (Seattle)

Self Evolving Agents, Agent Harness, Secure Code Generation with Agents

Purva Chiniya

Seller Partner Services (Seattle)

LLM Application in Economics, Growth

Jieyi Jiang

AWS (Austin)

LLMs, Optimization, ML Foundations

Anish Acharya

PXT (New York)

Agentic Evaluation

Yingqiang Ge

3:30 - 4:30 PM

Team

Domain/research

Amazonian

Amazon AGI Lab (SF)

Post-Training data for Agentic RL: Synthetic Data Generation, Eval-Driven Diagnosis, Targeted Data Generation for Web and Computer-Use Agents

Annika Huston

Japan Consumer Innovation (JCI) (Japan)

Machine Learning, Statistics, Causal Inference

Yuhao Wang

Generative AI Innovation Center (GenAIIC) (Seoul)

Physical AI, Agentic AI

Sanggyu Biern

WW Ops (SF)

Multimodal Perception, Large Language Models (LLMs), Vision-Language Models (VLMs), On-Device AI Systems

Yelin Kim

4:30 - 5:30 PM

Team

Domain/research

Amazonian

AWS (Bay Area)

Post Training LLM, Causal Inference

Shiva Kasiviswanathan

Generative AI Innovation Center (GenAIIC) (Seoul)

LLM Alignment, Reinforcement learning, Physical AI

Jack (Jaekyung) Cho

AWS (London)

Agentic AI, Physical AI, Multimodal Models

Orange Gao

AWS (London)

Agentic AI, GenAI, Reinforcement Learning

Diana Alvarado

Presentations:
9:30 - 10:00 AM Talk: Multi-node GPU clustering LLM training on AWS 101 - Daekeun Kim

11:00 - 11:30 AM Talk: Realtime computer use agent - HyungjunLee

11:30 AM - 12:00 PM Talk: Eval-driven diagnosis & targeted data generation for agentic RL - Annika Huston

1:30 - 2:00 PM Talk: Research infrastructure for agentic RL - Daisy Lin, Fred Robinson

2:00 - 2:30 PM Talk: Agentic harness: A framework for building reliable AI agent Systems - Diana Alvarado, Orange Gao

2:30 - 3:00 PM Talk: DOT-MoE: Differentiable optimal transport for MoEfication- Udbhav Bamba

3:30 - 4:00 PM Talk: Beyond LLM fine-tuning: Towards continual training without regressions - Mauricio Tec

Chat with scientists:
9:30 - 10:30 AM

Team

Domain/research

Amazonian

AWS SMGS Ops (Luxembourg)

AI/ML/Agentic Systems, Causal Inference, Foundational Model Post Training, Recommendation Systems

Laurent Mombaerts

AWS SMGS Ops (Luxembourg)

Representation Learning, LLM Fine-Tuning, Tabular Foundation Models

Jonathan Taws

10:30 - 11:30 AM

Team

Domain/research

Amazonian

AWS MPS Science (Seoul)

Large Language Models, Agentic AI

Sina Amini Niaki

AWS MPS Science (Austin)

Reliability on Generative Outputs, Uncertainty Quantification for LLMs and Agents, Conformal Prediction, Hallucination Mitigation for Generative Outputs

Kris Pan

JCI (Japan)

Machine Learning, Statistics, Causal Inference

Yuhao Wang

1:30 - 2:30 PM

Team

Domain/research

Amazonian

Prime Video (London)

Computer Vision, AV Foundational Models, VLMs

Benoit Vallade

Stores, Rufus, Personalization (Seattle)

LLM training, Long-Context, Personalization, Mid-Training, SFT, Post-Training

Hamed Bonab

Catalog System Services (CCS) Science (Seattle)

NLP, Cascade and Routing, Fine Tuning

Samira Mansoori

2:30 - 3:30 PM

Team

Domain/research

Amazonian

Advertising (Seattle)

AI/RL, Optimal Control, Causal Inference, Experiment Design

Yu Gan

Buyer Fraud (Seattle)

Agentic AI, LLMs, Reinforcement Learning, Interpretable Machine Learning

Nick Borodinov

Seller Partner Services (Seattle)

Uncertainty Quantification, Model Interpretability, Foundation Models

Brandon Feng

3:30 - 4:30 PM

Team

Domain/research

Amazonian

AWS (Austin)

LLMs, Optimization, ML Foundations

Anish Acharya

Buyer Fraud (Seattle)

Agentic AI, LLMs, Reinforcement Learning, Interpretable Machine Learning

Nick Borodinov

Catalog System Services (CCS) Science (Seattle)

NLP, Cascade and Routing, Fine Tuning

Diana Alvarado

Presentations:
9:30 - 10:00 AM Talk: Boosting solvability in RL - Silun Wang

11:00 - 11:30 AM Talk: Training agentic models for the harness ecosystem - Gradey Wang

11:30 AM - 12:00 PM Informational: Interviewing in the age of AI - Marisa Klee, Natalie Matushevsky

1:30 - 2:00 PM Talk: Intelligent robotics for everyone - Cagatay Cali, Yin Song

2:00 - 2:30 PM Talk: Teaching LLMs program semantics via symbolic execution traces - Stefan Zetzsche

3:00 - 3:30 PM Talk: Self improving agents with automated optimization - Bharathi Srinivasan, Visakh Madathil

3:30 - 4:00 PM Talk: Determinstic agentic safeguards with policy in Amazon Bedrock AgentCore - Bharathi Srinivasan, Visakh Madathil

Chat with scientists:
9:30 - 10:30 AM

Team

Domain/research

Amazonian

Amazon Special Projects (Seattle)

ML, LLM Pre-Training, LLM Post-Training, Generative Models, Recommendation Systems

Shang Shang

10:30 -11:30 AM

Team

Domain/research

Amazonian

AGI Autonomy (SF)

LLM, Computer Use Agents

Hyungjun Lee

AGI Lab (SF)

Reinforcement Learning, Evaluations

Gradey Wang

AGI (SF)

Reinforcement Learning

Frederick Robinson

1:30 - 2:30 PM

Team

Domain/research

Amazonian

Ads (Seattle)

Self Evolving Agents, Agent Harness, Secure Code Generation with Agents

Purva Chiniya

AWS (Austin)

LLMs, Optimization, ML Foundations

Anish Acharya

Stores, Rufus, Personalization (Seattle)

LLM training, Long-Context, Personalization, Mid-Training, SFT, Post-Training

Hamed Bonab

Buyer Fraud (Seattle)

Agentic AI, LLMs, Reinforcement Learning, Interpretable Machine Learning

Nick Borodinov

2:30 - 3:30 PM

Team

Domain/research

Amazonian

Prime Video (London)

Computer Vision, AV Foundational Models, VLMs

Benoit Vallade

AWS (Seoul)

Language Models, Agents

Chanho Soh

Korea AI Business Team (Seoul)

LLMs, VLMs

Daekeun Kim

3:30 - 4:30 PM

Team

Domain/research

Amazonian

Generative AI Innovation Center (GenAIIC) (London)

Generative AI

Zainab Afolabi

Buyer Fraud (Seattle)

Agentic AI, LLMs, Reinforcement Learning, Interpretable Machine Learning

Nick Borodinov

4:45 - 5:45 PM

Team

Domain/research

Amazonian

AGI Foundations Speech & Audio (Bay Area)

Speech/Audio Conversational, Pre-Training, Supervised Fine-Tuning, Omni Models, LLM, Diffusion, Representation Learning

Renard Korzeniowski

Sustainability (Seattle)

Foundation Models

Ruohong Li

Buyer Fraud (Seattle)

Agentic AI, LLMs, Reinforcement Learning, Interpretable Machine Learning

Nick Borodinov

Expo and Workshops

Expo Talk: Strands robots: Unifying robot control, simulation, and training behind natural language
July 6, 8:00 AM - 9:00 AM KST
Room: Hall B2
Speakers: Cagatay Cali, Yin Song

Abstract: Despite rapid advances in vision-language-action (VLA) models, deploying robot intelligence remains fragmented: different SDKs for different robots, different policy frameworks with incompatible interfaces, an unbridged simulation-to-reality gap, and training pipelines that demand specialist expertise . We present Strands Robots, an open-source Python SDK that unifies the complete robot lifecycle - simulation, control, training, and deployment - behind natural language. Our central contribution is a Policy abstraction layer with a plugin registry supporting VLA/WFM providers under a single three-method interface, enabling zero-code-change transfer between simulation and real hardware. We scale this abstraction across three axes: robot diversity ( 40+ bundled models from MuJoCo Menagerie spanning arms, humanoids, quadrupeds, and dexterous hands), simulation fidelity (three backends: MuJoCo CPU, Newton GPU-differentiable with 4,096+ parallel environments, and Isaac Sim with RTX rendering), and policy ecosystem breadth (from 42M-parameter ONNX humanoid controllers at 135 Hz to 14B-parameter world action models). Built on the Strands Agents framework, every capability is exposed as a tool callable via natural language, enabling AI agents to autonomously design scenes, run experiments, collect data, train policies, and deploy hardware. We demonstrate that this unified approach achieves practical results: GEAR-SONIC humanoid whole-body control on Jetson AGX Thor, and seamless integration with NVIDIA's GR00T N1. 7 , Cosmos 3 . Strands Robots   establishes a practical foundation for autonomous robot development where the barrier between idea and physical action is a single line of Python.
Expo Workshop: From digital agents to physical intelligence: The agentic harness as a unifying architectural pattern
July 6, 4:00 PM - 7:00 PM KST
Room: Hall C
Speakers: Diana Alvarado, Orange Gao, Xiaogang Wang, Minsoo Khang, Jaekyung Cho, Tatsuo Azeyanagi, Sanggyu Biern, Jie Zhao, Julija Bainiaksina

Abstract: You've built prompts. You've engineered context windows. But when your agent hallucinates in production, drops tools mid-task, or spirals in a loop—that's not a model problem. That's a harness problem.

In this workshop, we explore harness engineering in -depth: the discipline of building the runtime infrastructure that wraps a foundation model and turns it into a reliable, production-grade agent and how the same pattern translates to physical AI.

What we'll cover:

1. What a harness refers to. We trace where the term comes from, how the industry interprets it today, and why it's the layer that actually determines whether your agent works.
2. The Components & Why They Matter. We break down the six core components (context management, tool registry, verification loops, state & memory, safety controls, and observability) and show why getting these right matters more than picking the "best" model.
3. White boarding the Harness and production patterns .We map out real industry patterns(master loop, initializer-worker, handoff mesh, workflow graphs) grounded in real customer case studies.
4. The Harness Effect experiment. What happens when we hold the model fixed and change only the harness?,We'll go over this experiment and its results and will show how the execution layer alone can influence an agent's outcome, you also get to try it out
5. Physical AI: The Harness Goes Embodied. We'll cover how a robot's control loop (observe→encode → decide → act → repeat) uses the same harness pattern. We break this down into the core pillars of physical intelligence with real projects cases:
- Perception & Sequential Data: Handling multi-modal sensory inputs and managing continuous time-series state
- Robotics Foundation Model: Operating the central model to drive physical decision-making under real-world constraints.
- Simulation & Performance Evaluation: Establishing safe, rigorous testing environments to isolate and benchmark execution.

Come see what it takes to build an agent that actually works, then watch the same pattern navigate the physical world.
ICML 2026 The 6th Muslims in ML (MusIML) Workshop
July 9
Link: https://www.musiml.org/events/2026-ICML/index.html

The 6th Muslims in ML (MusIML) Workshop continues its tradition of success at top-tier conferences, providing a vibrant academic and social platform for the global Muslim community in machine learning. This workshop directly responds to ICML's call for Affinity Groups, focusing on the intersection of machine learning, fairness, and the global Muslim community to explore its unique challenges and opportunities. Our core objective is to foster high-quality original research, promote ethical AI solutions tailored to diverse cultural, linguistic, and economic contexts, and create an inclusive and safe academic space for Muslim researchers.

The MusIML community has an extensive track record of organizing successful events at premier conferences. Since 2020, we have organized 10 official events, including 5 workshops and 5 social and mentoring events. Our workshops have become a staple affinity event at both NeurIPS and ICML. The community has shown consistent growth, from approximately 100 attendees at our inaugural workshop at NeurIPS 2020 to accepting 60 papers with 300+ attendees at NeurIPS 2025, demonstrating our expanding influence and the increasing engagement of our community.
The 6th Muslims in ML workshop will be held in July 9, 2026, co-located with ICML 2026, at Room S300, COEX, Seoul, South Korea.
ICML 2026 Workshop on Generative and Agentic AI for Biology
July 10
Website: https://genbio-workshop.github.io/2026/

This workshop brings together leading researchers from machine learning, computational biology, and industry to explore both fronts — from the latest generative models for proteins, RNAs, and cells, to the emerging role of AI agents in experimental design and biological discovery.
ICML 2026 Workshop on Scalable Learning and Optimization for Efficient Multimodal AI Agents (SCALE)
July 10
Location: Seoul, South Korea

Link: https://scale-icml-2026.github.io/

Agentic systems are increasingly central to high-stakes computing platforms such as AI PCs, robotics, autonomous web interaction, and software maintenance, with their performance largely determined by how effectively they manage memory and context. Enabled by multimodal foundation models, these agents can coordinate human-like reasoning through structured agentic workflows, unlocking powerful capabilities across software development, web and mobile operations, and embodied manipulation. This progress points to the broad potential of multi-agent, multimodal systems to tackle complex real-world challenges. However, realizing this potential at scale remains difficult due to fundamental limits in algorithmic reasoning, memory-driven context understanding, the need for effective test-time training and scaling, and the challenge of deploying agents efficiently across heterogeneous AI hardware where different components must run on distinct compute fabrics.
ICML 2026 Workshop on Statistical Frameworks for Uncertainty in Agentic Systems
July 11
Website: https://agentic-uncertainty-icml2026.github.io/

Modern agentic pipelines are hierarchical compositions of models, tools, and subagents. This workshop studies how rigorous guarantees can support reliable routing, monitoring, stopping, and uncertainty reporting in such systems.
ICML 2026 Workshop on Weight-Space Symmetries
July 11
Link: https://www.weightsymmetry.com/
Location: Seoul, South Korea

Neural network weight spaces have a rich geometric structure, shaped by symmetries inherent to the architecture. Even the simplest MLP exhibits neuron permutation symmetries, meaning that swapping any two neurons in a layer along with their weights does not change the network function. Modern architectures introduce many more: attention mechanisms add continuous rotation symmetries, nonlinearities and normalization layers create scaling symmetries, and multi-head and mixture of experts architectures introduce permutation symmetries over heads and experts. Altogether, these symmetries shape the loss landscape and training dynamics as well as play an important role in model analysis, merging, and learning from model weights.

This workshop aims to deepen the fundamental understanding of weight-space symmetries and their effects, and to translate these insights into practical and scalable methods with diverse applications.
ICML 2025 Workshop on Foundation Models for Structured Data
July 11
Link: https://icml-structured-fm-workshop.github.io/

Structured data (tabular and time-series) underpins high-impact applications across finance, healthcare, enterprise decision-making, and climate modeling. Over the past two years, predictive foundation models tailored to structured data have emerged, enabling in-context learning and transfer across heterogeneous datasets and schemas, challenging the traditional “train per dataset” paradigm. Tabular and time-series foundation models share methodological similarities: pretraining on heterogeneous datasets, in-context learning, and transfer under schema and distribution shift. These similarities create natural synergies across the respective communities.

Building on the inaugural Foundation Models for Structured Data workshop at ICML 2025 (99 submissions; 500+ participants), FMSD @ ICML 2026 will unify the tabular and time-series communities around shared challenges in data curation, scaling, evaluation (including contamination), and real-world deployment (latency, memory, monitoring).
ICML 2026 Workshop on Connecting Low-Rank Representations in AI
July 11
Link: https://grigoris.ece.wisc.edu/workshops/colorai-icml-2026/

Location: Seoul, South Korea

At the heart of modern machine learning and artificial intelligence lies a simple observation: the data and computations we care about are almost never as complex as they first appear. A high-dimensional matrix rarely needs all its dimensions — it is low-rank, meaning a compact product of smaller matrices can faithfully represent it. This idea is extended to multi-dimensional arrays — tensors.

CoLorAI 2026 is designed to bring the communities advancing these ideas together. Building on our inaugural CoLorAI edition at AAAI 2025, this is the first workshop at ICML to bring all these communities to a single stage in Seoul, fostering the cross-pollination that drives breakthroughs.
ICML 2026 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM)
July 11
Link: https://adaptfm.gitlab.io/
Location: Seoul, South Korea

Foundation models (FMs) have achieved remarkable capabilities across language, vision, and multimodal tasks. However, their inference typically follows a rigid, one-size-fits-all paradigm where every input, regardless of complexity, passes through the same fixed architecture with identical computational cost. This inflexibility creates a fundamental mismatch between the diverse resource budgets encountered in real-world deployments and the static nature of model inference.

Adaptation can take many forms: compressing models to meet deployment budgets, designing flexible architectures that support multiple configurations from a single trained model, or making dynamic runtime decisions based on input complexity or resource availability. The central question we explore is: How can foundation model inference flexibly adapt to any resource budget, whether constrained by memory, compute, latency, energy, or cost, while maximizing output quality?
ICML 2026 Workshop on Generative and Agentic AI for Biology
July 10
Website: https://genbio-workshop.github.io/2026/

This workshop brings together leading researchers from machine learning, computational biology, and industry to explore both fronts — from the latest generative models for proteins, RNAs, and cells, to the emerging role of AI agents in experimental design and biological discovery.
ICML 2026 Workshop on Reinforcement Learning from World Feedback
July 10
Link: https://icml.cc/virtual/2026/workshop/54067
Location: Seoul, South Korea

The RLxF (Reinforcement Learning from World Feedback) Workshop at ICML 2026focuses on training RL systems using world-grounded signals (like efficiency, safety, and economic outcomes) rather than traditional human preference signals. It explores integrating heterogeneous, noisy, and delayed feedback into learning pipelines.
ICML 2026 Workshop on High-dimensional Learning Dynamics (HiLD)
July 10
Link: https://icml.cc/virtual/2023/workshop/21475

The 4th Workshop on High-dimensional Learning Dynamics (HiLD) takes place on July 10 at ICML 2026 in Seoul, South Korea. This year's workshop heavily focuses on the science of scaling, exploring power-law relationships between frontier model performance and resources like parameters, data, and compute.
Physics-guided policy optimization with self-distillation
Ke Wang, Yuning Wu, Haoran Liu, Enzo Jia, Devin Chen, Kai Wei
2026
Conversational AI

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2026 Fall Applied Science Internship - Reinforcement Learning & Optimization (Machine Learning) - United States, PhD Student Science Recruiting
US, WA, Seattle
Unlock the Future with Amazon Science! Calling all visionary minds passionate about the transformative power of machine learning! Amazon is seeking boundary-pushing graduate student scientists who can turn revolutionary theory into awe-inspiring reality. Join our team of visionary scientists and embark on a journey to revolutionize the field by harnessing the power of cutting-edge techniques in bayesian optimization, time series, multi-armed bandits and more. At Amazon, we don't just talk about innovation – we live and breathe it. You'll conducting research into the theory and application of deep reinforcement learning. You will work on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. You will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised, semi-supervised and unsupervised learning, reinforcement learning, advanced statistical modeling, and graph models. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Optimization, Programming/Scripting Languages, Statistics, Reinforcement Learning, Causal Inference, Large Language Models, Time Series, Graph Modeling, Supervised/Unsupervised Learning, Deep Learning, Predictive Modeling In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Reinforcement Learning and Optimization within Machine Learning. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on developing novel RL algorithms and applying them to complex, real-world challenges. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Develop scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Design, development and evaluation of highly innovative ML models for solving complex business problems. - Research and apply the latest ML techniques and best practices from both academia and industry. - Think about customers and how to improve the customer delivery experience. - Use and analytical techniques to create scalable solutions for business problems.
2026 Fall Research Science Internship - United States, PhD Student Science Recruiting
US, WA, Seattle
Do you enjoy solving challenging problems and driving innovation in research? Do you want to develop scalable models and apply machine learning techniques to guide real-world decisions? We are looking for builders, innovators, and entrepreneurs who want to bring their ideas to reality and improve the lives of millions of customers. As a Research Science Intern, you will apply advanced statistical techniques and emerging AI/ML technologies to solve complex problems, implement prototypes, and work with massive datasets. You'll find yourself at the forefront of innovation, shaping the future of Amazon's products, services, and operations. Imagine waking up each morning, fueled by the excitement of solving intricate problems that have a direct impact on Amazon's excellence. Your day might begin by collaborating with cross-functional teams, exchanging ideas and insights to develop innovative solutions. You'll immerse yourself in a world of data, leveraging your expertise in areas such as optimization, machine learning, statistical modeling, and algorithmic research to uncover hidden patterns and drive meaningful impact. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research, and mentorship from industry leaders. This immersive experience will sharpen your technical skills and cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Amazon has positions available for Research Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA, Arlington, VA Key job responsibilities • Conduct research activities including data collection, analysis, and interpretation under the guidance of senior researchers • Develop and test hypotheses using appropriate scientific methodologies and computational tools • Document findings, maintain detailed research records, and prepare reports summarizing results and insights • Collaborate with team members to troubleshoot challenges and refine experimental approaches • Participate in team meetings and present progress updates on assigned research projects A day in the life As a Research Science Intern, you'll immerse yourself in hands-on scientific work, collaborating with our research team on projects that span data analysis, experimental design, and computational modeling. Your day might include conducting literature reviews, running simulations, analyzing datasets, and participating in team discussions where your insights contribute to ongoing research initiatives. You'll have opportunities to present findings, learn from mentors, and develop practical skills in a supportive research environment that values curiosity and collaborative problem-solving.
2027 Applied Science Intern (Machine Learning, Recommender Systems), Amazon International Machine Learning
AU, VIC, Melbourne
Are you excited about leveraging and extending state-of-the-art Deep Learning, Information Retrieval, Natural Language Processing, Computer Vision algorithms to solve customer problems at the scale of Amazon? As an Applied Scientist Intern, you will be working in the Melbourne office in a fast-paced, cross-disciplinary team of experienced R&D scientists. You will take on complex problems, work on solutions that leverage existing academic and industrial research, and utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even deliver these to production in customer facing products. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Deep Learning and Generative AI - Contribute to research that could significantly impact Amazon operations - Collaborate with a diverse team of experts in a fast-paced environment - Present your research findings to both technical and non-technical audiences - Collaborate with scientists on writing and submitting papers to top ML conferences, e.g. NeurIPS, ICML, ICLR, AISTATS, ACL ICCV, CVPR, KDD. Key Opportunities: - Work in a team of ML scientists to solve applied science problems at the scale of Amazon - Access to Amazon services and hardware - Potentially deliver solutions to production in customer-facing applications - Opportunities to be hired full-time after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!
2027 Applied Science Intern (Computer Vision), Amazon International Machine Learning
AU, VIC, Melbourne
Are you excited about leveraging state-of-the-art Computer Vision algorithms and large datasets to solve real-world problems? Join Amazon as an Applied Scientist Intern and be at the forefront of AI innovation! As an Applied Scientist Intern, you'll work in a fast-paced, cross-disciplinary team of pioneering researchers. You'll tackle complex problems, developing solutions that either build on existing academic and industrial research or stem from your own innovative thinking. Your work may even find its way into customer-facing products, making a real-world impact. Please note: This internship is a duration of 6 months full time with a start date in Jan-March 2027. The successful intern is required to be based in Melbourne and relocation allowance will be provided if you are based outside of Melbourne. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Computer Vision and Machine Learning - Contribute to research that could significantly impact Amazon's operations - Collaborate with a diverse team of experts in a fast-paced environment - Collaborate with scientists on writing and submitting papers to Tier-1 conferences (e.g., CVPR, ICCV, NeurIPS, ICML) - Present your research findings to both technical and non-technical audiences Key Opportunities - Collaborate with leading machine learning researchers - Access Amazon tools and hardware (large GPU clusters) - Address challenges at an unparalleled scale - Become a disruptor, innovator, and problem solver in the field of computer vision - Potentially deliver solutions to production in customer-facing applications - Opportunities to become an FTE after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!
Postdoctoral Scientist, Amazon Robotics Research and AI Development
US, MA, N.reading
Amazon is looking for talented Postdoctoral Scientists to join the Research and AI Development team at Amazon Robotics for a one-year, full-time research position with an optional extension for a second year. This Postdoctoral Scientist will innovate in the areas of multi-agent path planning, dynamic optimal transport (OT), and explainable AI (X-AI) for DeepFleet Foundation Models. They will have the opportunity to develop optimal and scalable solutions for the world’s largest fleet of mobile robots, in addition to developing interpretability techniques for the DeepFleet FMs. At Amazon, we experiment and innovate relentlessly. Science is core in our offering to shoppers, advertisers and customers. Our scientists apply machine learning, optimization, and probabilistic modeling at scale to enhance customer experience, help advertisers reach relevant audiences, and support brand building. We are seeking talented scientists to invent new techniques in a variety of areas and innovate on behalf of shoppers, advertisers, and customers. Key job responsibilities In this role you will: -Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s diverse global science community. -Publish your innovation in top-tier academic venues and hone your presentation skills. -Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise. A day in the life On a typical day in this role, you will work to progress your research projects, meet with engineering, systems, and solutions stakeholders, brainstorm with other scientists on the team, and participate in team processes. You will follow your multi-agent path planning, dynamic OT, and X-AI projects through the entire life cycle of design, implementation, evaluation, analysis, and will communicate your findings and results through publications in top-tier academic venues. About the team The Research and AI Development team at Amazon Robotics is a multi-disciplinary science team that includes scientists with backgrounds in planning and scheduling, optimization, machine learning, and operations research. We develop novel planning algorithms and machine learning methods and apply them to real-word robotic warehouses, including: (1) Planning and coordinating the paths of thousands of robots (2) Dynamic allocation and scheduling of tasks to thousands of robots (3) Learning how to adapt system behavior to varying operating conditions and (4) Co-design of robotic logistics processes and the algorithms to optimize them.
Postdoctoral Scientist, Amazon Robotics Research and AI Development
US, MA, N.reading
Amazon is looking for a talented Postdoctoral Scientist to join the Fleet Science team at Amazon Robotics for a one-year, full-time research position with an optional extension for a second year. This Postdoctoral Scientist will advance AI-driven optimization of operations workflows for robotic fulfillment at scale. Research areas include automated optimization formulation that enables non-expert users to formulate, solve, and interpret complex optimization problems through natural language, intelligent solver configuration that adapts to problem structure for significant performance gains, and fleet-level AI for dynamic task allocation methods that coordinate decisions across large robot fleets in real time. The postdoc will have the opportunity to develop scalable solutions that democratize and accelerate optimization workflows for the world's largest robotic fulfillment network. At Amazon, we experiment and innovate relentlessly. Science is core in our offering to shoppers, advertisers and customers. Our scientists apply machine learning, optimization, and probabilistic modeling at scale to enhance customer experience, help advertisers reach relevant audiences, and support brand building. We are seeking talented scientists to invent cutting-edge techniques in a variety of areas and innovate on behalf of shoppers, advertisers, and customers. Key job responsibilities In this role you will: - Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon's vibrant and diverse global science community. - Publish your innovation in top-tier academic venues and hone your presentation skills. - Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise. A day in the life On a typical day in this role, you will work to progress your research projects, meet with engineering, systems, and solutions stakeholders, brainstorm with other scientists on the team, and participate in team processes. You will lead your AI-based optimization research through the full life cycle, from design and implementation to evaluation and analysis. Publication of findings in top-tier academic venues is expected. About the team The Fleet Science team at Amazon Robotics is a multi-disciplinary science team that includes scientists with backgrounds in planning and scheduling, optimization, machine learning, and operations research. We develop novel planning algorithms and machine learning methods and apply them to real-world robotic warehouses, including: (1) Planning and coordinating the paths of thousands of robots (2) Dynamic allocation and scheduling of tasks to thousands of robots (3) Learning how to adapt system behavior to varying operating conditions and (4) Co-design of robotic logistics processes and the algorithms to optimize them.
Postdoctoral Researcher – Visual Localization & Navigation | Amazon Last Mile
US, CA, Santa Cruz
Amazon is looking for talented Postdoctoral Scientists to join our research team for a full-time research position focused on visual localization and navigation for real-world applications. Our work focuses on developing next-generation assistive technologies and logistics platforms that rely on robust, scalable visual perception systems. We are building solutions that enable devices and agents to understand, localize within, and navigate complex real-world environments—from indoor spaces with dynamic layouts to large-scale outdoor settings. We are looking for Postdoctoral Scientists to work at the intersection of computer vision, SLAM, and scene understanding—supporting innovations that will be deployed to real systems at global scale. The core technical challenges include building metric-semantic maps of complex environments, performing robust visual relocalization under appearance change, maintaining long-term map consistency, and achieving accurate monocular localization using both geometric and learning-based approaches—all under real-time constraints on real hardware. The solution space is deliberately open-ended. We are looking for researchers who want to push the boundaries of visual localization and spatial AI—and see their work running on real platforms within months. Key job responsibilities In this role you will: * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise. A day in the life 0
Senior Applied Scientist, Selling Partner Support Engagement
US, WA, Seattle
Amazon's Selling Partner Support handles tens of millions of contacts annually worldwide. The Titans Science team is transforming this experience by building AI agents that autonomously resolve seller issues, learn from every interaction, and continuously improve with minimal human intervention. These agents reason, remember, and adapt — from understanding the seller's context and selecting the right solution, to routing contacts optimally, automating resolution end-to-end, and augmenting associates with AI when human judgment is needed. We do this in deep partnership with multiple engineering and product partners. We are looking for a Senior Applied Scientist who wants to work at the intersection of reinforcement learning, agentic architectures, and large-scale production systems. You will be directly connected to the problems sellers face every day, translating real customer pain into science solutions that operate at massive scale. You will frame ambiguous business challenges as tractable ML problems to shipping systems that measurably improve millions of seller interactions. Key job responsibilities - Own end-to-end research and development of RL-based agent improvement systems — from problem formulation through production deployment and impact measurement. - Design novel approaches to preference learning, reward modeling, and policy optimization in the context of conversational agents operating over real-world tools and APIs. - Build and maintain evaluation frameworks that measure agent quality across multiple dimensions: helpfulness, correctness, safety, and alignment with operational standards. - Collaborate with a team of scientists that work on forefront of Natural Language Understanding, Optimization, Machine Learning and Statistics - Partner with 10+ engineering teams to deploy models into production systems serving sellers worldwide. - Publish research at top venues (NeurIPS, ICML, EMNLP, AMLC) — the complexity of our problems produces publishable work, and we actively support it. - Raise the scientific bar through rigorous peer review, mentorship of junior scientists, and contribution to hiring. A day in the life You read the latest research papers and implement novel techniques by building rapid prototypes using AI-assisted coding tools, then taking what works from prototype to production. You collaborate closely with product managers and engineering teams to translate seller pain points into deployed science solutions. You influence leadership by bringing the state of the art to strategic decisions about where the organization invests, and you drive the science roadmap for your domain — identifying new research directions, proposing experiments, and making the case for what to build next. You mentor other scientists on the team, raising the bar on rigor and execution and get mentored by Principals across the org. Finally, you attend meetings with other Amazonians to stay connected to the seller experience by understanding the real problems sellers face so your models solve what actually matters. About the team Titans Science is a growing team of scientists building the AI that powers Amazon's seller support experience. We operate in across capabilities such as Agentic Systems, Knowledge Retrieval & Query Understanding, and Content Intelligence & Automation, each owning distinct problem spaces but sharing evaluation infrastructure and research insights. We work backwards from business problems, deeply understanding the problem space and domain, defining gold-standard datasets, success metrics, and guardrails. This lets us run parallel experiments, compare approaches rigorously, and ship the best Science models to production. We publish at internal conferences and external venues, and we actively invest in research that compounds over multiple product cycles. The team sits in Seattle and operates with high autonomy. Scientists own their domains end-to-end, from problem framing through production deployment. We value speed over perfection, scientific rigor over polish, and experimentation over debate. We value diverse experiences. Even if you do not meet all of the preferred qualifications listed above, we encourage you to apply. The team fosters an inclusive learning culture where individual growth is a priority — you will find mentorship, knowledge-sharing, and career-advancing resources here.
Principal Applied Scientist, Amazon Payments
US, WA, Seattle
We are looking for a Principal Applied Scientist for Amazon Payments AI/ML Team, which contributes science and science-related engineering work for Amazon’s Payments Artificial Intelligence services (e.g. Amazon Payments Recommendation, Prediction, GEN AI platform to enable SOP automation and new projects already underway). In this role, you will work with your peers and senior management to set the direction for Amazon’s AI efforts. Our mission is to put the power of AI in the hands of every developer. You will be responsible for mentoring a team of applied scientists. You will be responsible for creating a strong environment for applied scientists, with a focus on recruiting, retaining and developing top talent. You will partner with engineering leaders to deliver remarkable new Amazon Payments services and features that leverage Machine Learning and GenAI. As a Principal Applied Scientist, you will identify research directions, create roadmaps for forward-looking research and communicate them to senior leadership, and work closely with engineering teams to bring research to production. You will work with teams of talented scientists, and fill the ranks by attracting the best scientists in machine learning, e.g. Amazon payments recommendation and natural language processing for SOP automation. You will work with talented peers and leverage Amazon’s heterogeneous data sources and large-scale computing resources.
Principal Applied Scientist, Sponsored Products and Brands
US, WA, Seattle
Amazon's advertising business has grown exponentially over the past years, helping connect sellers and vendors to shoppers who may be interested in their products. Our ad products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products that leverage state of the art AI technologies. The Sponsored Products and Brands team is seeking a Principal Applied Scientist to lead the development and implementation of generative AI solutions for ad allocation and ranking on Amazon search pages. This role will be instrumental in revolutionizing how we match ads with customer intent and shopping behavior. Key job responsibilities * Design and develop novel generative AI architectures for real-time ad allocation, focusing on both efficiency and effectiveness at massive scale * Lead research initiatives in applying large language models and multimodal AI to understand deep semantic relationships between ads, queries, and user behavior * Create innovative approaches to leverage GenAI for dynamic ad placement optimization while maintaining strict latency requirements * Collaborate with cross-functional teams to integrate GenAI solutions into existing advertising systems * Author research papers and technical documentation, contributing to the broader scientific community
Principal Applied Scientist, Sponsored Products and Brands
US, CA, Palo Alto
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Sponsored Brands team at Amazon is building the next generation of brand advertising products and platforms for advertisers using Gen AI. This role presents an opportunity to join us at the ground floor of this transformation, which is a core part of Amazon's overall business strategy. You will be the Gen AI applied science leader that will determine what products and ads show up in the most critical ad placements across Amazon. You'll have opportunities to deliver at the highest technical level, working with science, product, engineering, and UX, all directly connected to a marketplace of supply and demand. In this highly visible role, you'll collaborate across multiple stakeholders within Ads and Retail. We are seeking a Gen AI Applied Science inventor who has strong product sense, with a proven track record with hands on science vision and execution while building high impact products from the ground up. you will have the opportunity to apply your deep subject matter expertise in the area of ML, LLM and GenAI models. You will invent new product experiences that enable novel advertiser and shopper experiences. This role will work on bringing state-of-the-art GenAI models to production. You will define the long-term science vision for our advertising business, driven by our customer’s needs, and translate it into actionable plans for our team of applied scientists and engineers. Key job responsibilities You will play a pivotal role in managing projects at all stages: inception, design, development, deployment and subsequent improvements. You will tackle challenging problems and coordinate high profile projects across multiple teams to ensure customer and business goals are met. You will interface across product, science, and engineering teams within the advertising and retail organizations. You will drive mechanisms that allow the team to move quickly and deliver strong results. You will operate with a high degree of autonomy and efficiency. You'll be responsible for project management - prioritize, plan projects and features, manage partners, and track external commitments. You will recommend alternative technical approaches and partner with product, science, and engineering teams to meet timelines. You will actively role model the use of GenAI to make the team more efficient. About the team We are on a mission to make Amazon the best in class destination for shoppers to discover, engage, and purchase relevant products, from brands that are relevant to them. In this role, you will design and implement Gen AI solutions that help millions of advertisers create more effective ad campaigns with intelligent recommendations, while improving the overall experience at Amazon's global scale. Our team invents, defines, and delivers advertising products that drive brand discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon Store businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, fast-paced, and collaborative team with an entrepreneurial spirit.
Principal Applied Scientist, Advertiser Growth, Amazon Sponsored Products & Brands
US, CA, Palo Alto
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities The Principal Applied Scientist in Advertiser Guidance team will lead 50+ builders in pioneering a new generation of agentic AI applications for Amazon advertisers. You will define and lead the science roadmap of developing agentic experiences for recommendations and guidance delivered to +1.6MM Sponsored Products and Brand advertisers across multiple channels (Ad Console, Sales-managed, and 3P partners). You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. As a Principal Scientist, you will play a critical role in elevating the team’s scientific and technical rigor, identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. You will communicate learnings to leadership and mentor and grow Applied AI talent across the Ads Org.
2026 Applied Scientist Intern, Amazon University Talent Acquisition
DE, BE, Berlin
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK). Please note these are not remote internships.
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