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How AI is changing the nature of mathematical research
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How Amazon reworked its fulfillment network to meet customer demand
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Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law
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The fuel of the future is already here: Why TRISO matters
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How AI is changing the nature of mathematical research
How Amazon reworked its fulfillment network to meet customer demand
Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law
The fuel of the future is already here: Why TRISO matters

Customer-obsessed science

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Research areas

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Automated reasoning
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Information and knowledge management
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Operations research and optimization
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From the blog

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Technical deep-dives and perspectives from our scientists.
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Featured news

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Coming soon: Season 2
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
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AWS and Hopkins Engineering announce database for AI/ML antibody design
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
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2026 Amazon Nova AI Challenge: Trusted Software Agents track
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
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Amazon launches $68 million AI PhD Fellowship program
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.

Publications

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  • PINA: Leveraging side information in eXtreme multi-label classification via predicted instance neighborhood aggregation
    Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu
    ICML 2023
    2023
    The eXtreme Multi-label Classification (XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and
    Machine learning
  • Simplifying distributed neural network training on massive graphs: Randomized partitions improve model aggregation
    Jiong Zhu, Aishwarya (Aish) Reganti, Eddie Huang, Charles Dickens, Nikhil Rao, Karthik Subbian, Danai Koutra
    TKDD 2024
    2023
    Conventional distributed Graph Neural Network (GNN) training relies either on inter-instance communication or periodic fallback to centralized training, both of which create overhead and constrain their scalability. In this work, we propose a streamlined framework for distributed GNN training that eliminates these costly operations, yielding improved scalability, convergence speed, and performance over
    Machine learning
  • Lengthen your attribution window: Which digital ads have most long-term impact?
    Vivian Qin
    Applied Marketing Analytics (AMA)
    2023
    Brands usually invest in a portfolio of digital ad products for brand consideration and conversion, and their performance is commonly evaluated with ad - attributed metrics. However, these metrics limit the measurement of advertising effectiveness within a short time window, typically of two weeks. Therefore, they could underestimate the total effect if some ad products' efficacy lasts beyond the measurement
    Economics
  • Critical learning periods for multisensory integration in deep networks
    Michael Kleinman, Alessandro Achille, Stefano Soatto
    CVPR 2023
    2023
    We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early phases of training. Interfering with the learning process during this initial stage can permanently impair the development of a skill, both in artificial and biological systems where the phenomenon is known as a critical learning period
    Computer vision
  • MESOB: Balancing equilibria & social optimality
    Xin Guo, Lihong Li, Sareh Nabi, Rabih Salhab, Junzi Zhang
    KDD 2023 Workshop on Multi-Armed Bandits and Reinforcement Learning (MARBLE), ICML 2023 Workshop on The Many Facets of Preference-based Learning

    2023
    Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large number of anonymous agents, and the other is the intricate interplay between competition and cooperation. To model such complex systems, we propose a novel and tractable bi-objective optimization formulation with mean-field approximation
    Economics
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Amazon Research Awards
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
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Amazon Nova AI Challenge
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
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Research collaborations
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
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Work with us

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Senior Applied Scientist, International Machine Learning
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
Manager, Applied Science, RBS Tech
IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and visual), supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, product similarity, using GenAI, LLMs, NLP and Computer Vision. Key job responsibilities As an Applied Science Manager, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will Lead scientists on the team and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will create the environment in the team to file for patents and/or publish research work where opportunities arise. You will impact the large product strategy, identifies new business opportunities and provides strategic direction to the team.
Machine Learning Scientist / Applied Scientist, EU Prime and Marketing Analytics & Science (PRIMAS)
ES, M, Madrid
Are you interested in changing how Amazon does marketing — moving beyond platform-optimized broad reach to campaigns that find the right customer, at the right moment, using Amazon's unmatched 1P data? We are seeking an Applied Scientist to join PRIMAS (Prime & Marketing Analytics and Science). In this role, you will design and run the experiments that answer the foundational question for EU marketing: does adding 1P audience signal on top of Value-Based Optimization (VBO) improve marketing efficiency — and if so, for which customer cohorts, on which surfaces, and at what scale? Amazon's current marketing model is largely platform-led: we set objectives and let platforms optimize toward conversion. This approach works well for broad acquisition but systematically underserves lifecycle goals — it cannot distinguish between a Bargain Hunter who will never pay full price and a high-potential customer one nudge away from becoming a Prime member. This role sits at the center of changing that. You will build the 1P audiences, design the experiments that test them, and generate the evidence that guides how Amazon allocates hundreds of millions in marketing spend. Year 1 is an experimentation year. You will deploy 1P audiences across multiple surfaces and channels — Meta, Google, Amazon Display Ads — and measure incrementally against VBO baselines. The goal is not to replace platform optimization but to understand when and where the combination of 1P signal + VBO outperforms VBO alone, and to build the experimental infrastructure that makes this learning scalable. Key job responsibilities 1P Audience Development & Experimentation: - Build and validate 1P audience segments from Amazon behavioral, transactional, and lifecycle data - Design experiments that isolate the incremental effect of 1P audience signal over platform VBO baselines - Deploy audiences across activation surfaces and establish measurement standards that make cross-surface comparison valid Causal Measurement & Incrementality: - Apply causal inference methods to measure the true incremental lift of audience-based targeting vs. VBO - Develop power analysis frameworks and guardrails that enable rapid experimentation without underpowered or conflated tests - Deliver optimization recommendations grounded in experimental evidence: which cohorts respond, which surfaces deliver, which creative strategies drive behavior change Scaling the Learning: - Build reusable audience and measurement frameworks that can be deployed across campaigns and channels — year 1 experiments should produce infrastructure, not one-off analyses - Document experimental learnings in a way that informs both the 2026 roadmap and the business case for investing further in 1P audience capabilities in 2027+ - Partner with engineering and PMT to translate validated audience prototypes into production-ready solutions that scale beyond the experimentation phase About the team The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.
Data Scientist, Security Issue Management
US, WA, Seattle
Are you interested in building Agentic AI solutions that solve complex builder experience challenges with significant global impact? The Security Tooling team designs and builds high-performance AI systems using LLMs and machine learning that identify builder bottlenecks, automate security workflows, and optimize the software development lifecycle—empowering engineering teams worldwide to ship secure code faster while maintaining the highest security standards. As a Data Scientist on our Security Tool team, you will focus on building state-of-the-art ML models to enhance builder experience and productivity. You will identify builder bottlenecks and pain points across the software development lifecycle, design and apply experiments to study developer behavior, and measure the downstream impacts of security tooling on engineering velocity and code quality. Our team rewards curiosity while maintaining a laser-focus on bringing products to market that empower builders while maintaining security excellence. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in builder experience and security automation, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform how builders interact with security tools and how organizations balance security requirements with developer productivity. Key job responsibilities Design and run rigorous experiments to evaluate and improve security tooling performance, builder experience, and adoption across hundreds of thousands of builders, multiple security tools, and diverse business verticals. Lead the end-to-end lifecycle of data science and ML models — from research and experimentation through production launch — including defining success metrics, obtaining stakeholder sign-off, and managing rollout. Conduct online and offline analyses to measure the real-world impact of security tooling improvements beyond adoption metrics, including downstream effects on vulnerability resolution, builder productivity, and organizational security posture. Develop and deploy production-grade machine learning and statistical models using Python, SQL, and related tools to automate insights, detect patterns, and drive decision-making across STF's security tool ecosystem. Perform large-scale exploratory data analysis on builder feedback, ticket resolution, tool usage, and customer satisfaction data to uncover patterns, identify opportunities, and inform product and tooling decisions. Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels, including STF leadership metric reporting and customer satisfaction publications. Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues. A day in the life Morning - Review overnight pipeline health — nudge systems, ticket classification models, and adoption dashboards running as expected - Join daily standup with the SDI team to align on priorities and flag blockers - Dive into exploratory analysis — investigating a spike in unresolved tickets or segmenting builder feedback to understand adoption gaps Midday - Partner with security tool owners (e.g., Shepherd, Talos, Scorecard) to review experiment results — did the latest nudge improve resolution rates? - Translate findings into actionable recommendations for leadership reviews or WBR updates - Analyze CSAT survey data to surface emerging dissatisfaction themes Afternoon - Write production code — building features for the classification pipeline, optimizing SQL for the metrics scorecard, or iterating on a model for predicting resolution timelines - Collaborate with STF stakeholders to define success metrics for an upcoming model launch - Document findings, update trackers, and queue next steps About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
Senior Applied Scientist, Catalog System Services Science
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product relevant information at unprecedented scale with Frontier Models and Agents. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity ranging from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Enriching product information requires sophisticated models that reason across text, images, and structured data, all while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Catalog System Services Science team is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval. In essence, translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging frontier models, and agentic architectures to enrich catalog information at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment, processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor, building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community - publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
Psychometrician, AWS Training & Certification
US, VA, Arlington
The AWS Certification team is seeking a Psychometrician with experience working with criterion-referenced assessment programs to support a large global AWS Certification and Credentialing program. In this role, you will support all psychometric aspects of exam development and operation, including job analyses, standard setting, automated test assembly, item and test analyses, optimal item bank design, quality assurance, and project planning. You will work closely with a team of psychometricians, subject matter experts, certification exam program managers, publishing, delivery, security, and product management teams to support ongoing analyses of exam and credential data. To be successful in this position, you must be highly motivated, creative, detail oriented, and a self-starter who is able to think big, execute, ensure high quality, yet stay focused on the details. Key job responsibilities • Conduct Job Task Analysis (JTA) workshops and post-JTA survey analyses to define the blueprint and test specifications for new certifications or updates to existing certifications • Conduct standard setting studies to set the passing score for exams and credentials • Run item analysis to evaluate quality and performance of exam items • Use automated test assembly procedures to assemble forms or item pools • Work with content development to track item bank trends and optimize the health of item banks • Support the development of a cloud-based analytics and reporting system • Partake in development and performance analysis of credentials • Interpret and clearly communicate the results of analyses to stakeholders through written and oral reports • Follow the accreditation standards set by ISO/IEC:2012 17024 and the National Council for Certifying Agencies (NCCA) as they relate to valid psychometric practices • Contribute to the development and execution of the strategic goals regarding the AWS certification and credentialing program. • Consult with leadership, internal staff, external consultants, and industry leaders regarding advancement of current offerings
Applied Scientist, JP OPS STAR
JP, 13, Tokyo
Every day, Amazon Japan delivers millions of packages to customers' doors. Behind every routing decision, capacity plan, and network design is a modeling problem — and the science behind how those models learn, generalize, and improve in production is where you come in. JP OPS STAR Foundation is the applied science and engineering team that builds the decision systems powering Amazon Japan's transportation operations. We sit at the intersection of generative AI, large-scale optimization, and graph-based learning — developing models that turn complex operational structure into actionable intelligence. As an applied scientist, you will formulate and solve research problems that directly shape operational outcomes. Your work will span: - LLM-based agent systems — designing agentic architectures with structured evaluation frameworks that measure reasoning quality, tool use accuracy, and task completion under real operational constraints - GPU-optimized model serving and training — developing pipelines for large model inference and fine-tuning, with rigorous benchmarking of latency, throughput, and cost trade-offs - Graph representation learning — building graph neural network models that capture logistics network topology and learn node/edge representations for downstream prediction and optimization tasks You will own problems end-to-end: from formulation and experimentation through deployment and continuous measurement in production. We expect scientific rigor — well-designed experiments, proper baselines, quantified uncertainty — and the engineering judgment to make your methods work reliably at scale. This is a high-autonomy, high-impact role. You will collaborate with data engineers, BIEs, and operations leaders, and you will have the freedom to define your research agenda within our problem space. We value scientists who publish and share their work, and who treat production performance as ground truth for their ideas. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. Key job responsibilities - Formulate and solve modeling problems for LLM-powered agent systems (MCP servers, text-to-SQL, RAG), designing validation methods and confidence scoring for reliable outputs under operational constraints. - Design evaluation frameworks for AI agents: define metrics for reasoning quality and task completion, build backtesting pipelines, and develop automated regression detection. - Research and implement graph neural network architectures to learn structural representations of logistics networks; develop training methodology and evaluation protocols for production deployment. - Develop GPU-optimized pipelines for model training and inference, with rigorous benchmarking of latency, throughput, and cost trade-offs. - Own the scientific lifecycle end-to-end: problem formulation, experimental design, deployment, and continuous measurement against real operational outcomes. - Collaborate with data engineers and operations stakeholders to validate model outputs and quantify business impact. A day in the life - Diagnose a confidence-score drift in the agent eval dashboard, trace the root cause to a schema change, and redesign the validation logic to be schema-invariant. - Experiment with a graph attention architecture on the logistics network; benchmark against the baseline embedding on a downstream prediction task. - Profile a GPU training job to unblock a scaling experiment, identify a memory bottleneck, and restructure the data loader to halve iteration time. - Present model accuracy trends to operations leaders and recommend threshold adjustments based on the data.
Applied Scientist, AWS Automated Reasoning
US, TX, Austin
Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why Amazon Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Automated Reasoning, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest automated reasoning challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
Sr. Applied Scientist, AWS Automated Reasoning
US, WA, Seattle
Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why Amazon Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Automated Reasoning, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest automated reasoning challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
Sr. Applied Scientist, Fauna Robotics
US, CA, Sunnyvale
We are seeking an Applied Scientist to focus on Robotics Localization, Mapping, and Geometric Reconstruction. In this role, you'll research and build advanced SLAM and World Reconstruction algorithms that enable robots to know its pose in the world and fulfill various tasks in complex and dynamic home environments. You'll work across a broad spectrum of visual inertial odometry, 3D pose estimation, mapping, and geometric reconstruction problems to build robust solutions that support autonomous decision making, task planning, navigation, and manipulation. Key job responsibilities - Develop and implement robust robot state estimation, pose estimation, mapping, and reconstruction algorithms. - Build simulation-based and on-robot evaluation frameworks with comprehensive benchmarks and metrics for systematic evaluation of our spatial intelligence stack. - Lead scientific and technical projects across the spatial intelligence team. - Collaborate with navigation, manipulation, and other teams to ensure seamless integration of localization, tracking, and mapping capabilities. - Mentor junior scientists and engineers to solve real-world robotics problems. About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.
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