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INFORMS 2025
October 26 - 29, 2025
Atlanta, GA

Overview

Held each fall, the annual meeting brings together over 6,000 people to the world's largest operations research and analytics conference. It features more than 800 sessions and presentations, opportunities to meet with leading companies, universities and other exhibitors, an onsite career fair connecting top talent with over 100 organizations at the forefront of O.R. and analytics application, and other networking and educational events. The theme for the INFORMS Annual Meeting this year is “Smarter Decisions for a Better World” which encapsulates the core ethos of the organization and the mission it strives to achieve.

Sponsorship Details

Amazon organizing committee

Keynotes & Colloquiums

INFORMS 2025 Doctoral Student Colloquium (DSC)
October 25, 8:00 AM - 5:45 PM EDT
Website: Link

Presentations:
"Industry Job Search and Early Career Advice", 11:30 - 12:30pm, presented by Yigit M. Arisoy, Kiran Chahar, James Hungerford, Andreea Popescu

"Academia or Industry?", 1:45 - 2:45pm, presented by Ken Fordyce, Philip Kaminsky, and Ioana Mazare

Room: TBA
OR/MS in an Ever-Evolving Environment: Perspectives from INFORMS Fellows
October 27, 5:45 PM - 6:45 PM EDT
INFORMS Keynote Fellows Panel

Website: Link

Room: Bldg A Lvl 4 A411

The INFORMS Fellows represent a set of community leaders from a broad swath of INFORMS members. Come hear from some of these leaders in our field about what they view as the current and future of OR/MS. We have gathered four INFORMS Fellows with broad range of experience and knowledge for a panel discussion.

Topics may include:
1. AI in teaching, publishing
2. Connections between industry/academia
3. Reduced access to federal funding
4. The most interesting research directions in…optimization, stochastics?
5. What tools will be the most useful for students in supply chain, health care, finance, service systems?

Sessions

Labor Planning and UTR Process Optimization
October 26, 8:00 AM - 9:15 AM EDT
Website: Link

Session Chair: Elcin Cetinkaya

Presentations:
"Optimizing the allocation of outbound work to constrained resources in Amazon Fulfillment Centers", 8:00 - 8:15am, presented by Kevin Bunn

"Prior day labor planning in Amazon Fulfillment Center", 8:15 - 8:30am, presented by Yingqiu Zhang and Andy Johnson

"Optimizing order picking for Amazon Robotics Fulfillment Centers", 8:30 - 8:45am, presented by Ignacio Neira

"Robust and interpretable outbound resource plans", 8:45 - 9:00am, presented by Aaron Herman

"Computer vision integrated warehouse emulation for bulk package flow", 9:00 - 9:15am, presented by Sumant Joshi and Chin-Yuan Tseng

Room: Bldg A Lvl 4 A403
Reliable and Resilient Facility Logistics
October 26, 8:00 AM - 9:15 AM EDT
Website: Link

Session Chair: Ritesh Ojha

Presentation:
"Robust outbound load planning with volume splitting for parcel carriers", 8:45 - 9:00am, presented by Ritesh Ojha

Room: Bldg A Lvl 4 A411
Market Design, Sustainability, and the Environment
October 26, 8:00 AM - 9:45 AM EDT
Website: Link

Presentations:
"Impact of the maintenance in power plants and data-driven optimal maintenance scheduling", 9:00 - 9:15am, presented by Abdullah Coskun

Room: Bldg B Lvl 3 B315
Inventory Management at Amazon
October 26, 11:00 AM - 12:15 PM EDT
Website: Link

Session Chair: Chun Ye

Presentations:
"Managing inventory flows in a multi-echelon network - A simulation study", 11:00 - 11:18am, presented by Zihao Li

"Inventory placement planning - Overcoming the limitations of product grouping", 11:18 - 11:36am, presented by Ishan Bansal

"Optimizing inventory placement in speed-sensitive fulfillment networks with consumer substitution behavior", 11:36 - 11:54am, presented by Katja Meuche, Cristiana Lara, Louis Faugere, and Benoit Montreuil

"Inventory placement optimization with value function approximations", 11:54 - 12:12pm, presented by Chun Ye

Room: Bldg A Lvl 4 A403
Optimization and Analytics for Decision-Making in Healthcare
October 26, 1:15 PM - 2:30 PM EDT
Website: Link

Presentations:
"Inventory optimization for pharmaceutical fulfillment networks: A predictive and prescriptive analysis at Amazon Pharmacy", 1:33 - 1:45pm, presented by Yinsheng Wang, Alexandre Alves, Pierre Bhoorasingh, Cristobal Pais, Xin Tang, and Shan Liu

Room: Bldg B Lvl 3 B303
Inbound Supply Chain Planning and Execution
October 26, 1:15 PM - 2:30 PM EDT
Website: Link

Session Chair: Daniel Ulch

Presentations:
"Amazon distribution centers picking optimization", 1:15 - 1:40pm, presented by Prem Kumar Viswanathan and Shuocheng Guo

"Inbound inventory allocation in a tiered fulfillment network with storage distribution centers", 1:40 - 2:05pm, presented by Tolga Cezik, Di Wu, Stephen Graves, Daniel Chen, and Jason Acimovic

"When to split: Optimizing container breaking and inventory distribution decisions in an inbound supply chain", 2:05 - 2:30pm, presented by Daniel Ulch and Marcus Poggi

Room: Bldg A Lvl 4 A403
Optimal Sourcing at Amazon
October 26, 1:15 PM - 2:30 PM EDT
Website: Link

Session Chair: Ozge Sahin

Presentations:
"Just-in-time-aware long lead buying policy", 1:15 - 1:30pm, presented by Muhong Zhang, Yuguang Wu, Xiuli Chao, and Huseyin Topaloglu

"Title TBD", 1:30 - 1:45pm, presented by Thiago Mosqueiro

"Multi-period optimization models for distribution center inventory management under uncertainty", 1:45 - 2:00pm, presented by Caner Taskin and Alp Muharremoglu

"Structure-informed deep reinforcement learning for inventory management", 2:00 - 2:15pm, presented by Alvaro Maggiar

"Pareto Frontier profiling for order assignment optimization at Amazon", 2:15 - 2:30pm, presented by Neo Huang, Sherief Reda, Jikai Zou, and Andrea Qualizza

Room: Bldg A Lvl 4 A402
Innovations in Fulfillment and Logistics Operations
October 26, 2:45 PM - 4:00 PM EDT
Website: Link

Session Chair: Opu Islam

Presentations:
"Optimizing pick operations through distributed inventory assignment in fulfillment centers", 2:45 - 3:00pm, presented by Opu Islam, Michael Caldara, and Mouhacine Benosman

"Scheduling jobs for print on demand", 3:00 - 3:15pm, presented by Alfredo Nantes, Prem Viswanathan, and Yaoguang Zhai

"Optimizing delivery speed through shipping incentives", 3:15 - 3:30pm, presented by Prasanna Venkatesh Ramadas, Bhaskar Vooradi, and Krishna Pendyala

Room: Bldg A Lvl 4 A402
TSL Data-driven Research Challenge (Award Session 1)
October 26, 2:45 PM - 4:00 PM EDT
Website: Link

Presentations:
"Data-driven optimization for meal delivery: A reinforcement learning approach for order-courier assignment and routing at Meituan", 2:45 - 3:03pm, presented by Ramon Auad, Tomas Lagos, and Felipe Lagos

Room: Bldg A Lvl 4 A407
Last Mile Planning and Operations
October 26, 4:15 PM - 5:30 PM EDT
Website: Link

Session Chair: Chengliang Zhang

Presentations:
"Labor capacity planning in last-mile operations", 4:15 - 4:33pm, presented by Chengliang Zhang and Luying Sun

"Robotic solution to improve transportation utilization", 4:33 - 4:51pm, presented by Zhikun Gao, Andrew Bruce, and Jochen Koenemann

"Sequential testing for operations experiments and resource ramp up", 4:51 - 5:09pm, presented by Brayan Ortiz

"Closing the gap between scheduling and execution of under the roof processes", 5:09 - 5:27pm, presented by Nikolaos Lappas

Room: Bldg A Lvl 4 A403
Thriving in Industry: Leveraging AI and Analytics for Career Success
October 26, 4:15 PM - 5:30 PM EDT
Website: Link

Panelists: Jia Liu, Shixiang Woody Zhu, Yigit Arisoy, Shan Ba, Ryan Lekivetz, Liu Richard, and Xin Ma

Room: Bldg A Lvl 3 A302
Edelman Finalist Reprise
October 27, 8:00 AM - 9:15 AM EDT
Website: Link

Presentations:
"Regionalize and scale: Amazon network design for faster and cheaper delivery", 8:35 - 9:10am, presented by Cristiana Lara, Ling Zhang, and Shahbaaz Mubeen

Room: Bldg A Lvl 4 A402
Re-Inventing Labor Planning at Amazon
October 27, 11:00 AM - 12:15 PM EDT
Website: Link

Session Chair: Martin Savelsbergh

Presentations:
"Associate capacity forecasting", 11:00 - 11:15am, presented by Selin Tosun and Jeronimo Callejas

"Optimizing capacity: Workforce regionalization", 11:15 - 11:30am, presented by Hadi Panahi, Jeronimo Callejas, and Rachel Rutkowski

"Optimizing labor use and availability on the day of operations", 11:30 - 11:45am, presented by Nayeon Kim, Ramon Auad, and Zeynep Sargut

"Conceptualizing labor planning as a dynamic process: Developing a versatile qualitative dataset to support Large Language Models", 11:45 - 12:00pm, presented by Alana Scholl

"Large Language Models for model exploitability: A use-case in labor planning", 12:00 - 12:15pm, presented by Anis Ben Said, Roman Levkin, Thomas Fillebeen, David Dong, and Brian Salisbury

Room: Bldg A Lvl 4 A407
Site Capacity Management & Optimization
October 27, 11:00 AM - 12:15 PM EDT
Website: Link

Session Chair: Francisco Castillo Zunino

Presentations:
"An unconstrained very short term forecast for the Amazon Fulfillment Network", 11:00 - 11:25am, presented by Filippo Fedeli, Thomas Ovestad, Giorgio Polla, Nicholas Richardson, Inaki Estella Aguerri

"Site capacity optimization: Defining operational capacity frontiers", 11:25 - 11:50am, presented by Francisco Castillo Zunino, Matias Siebert, Filippo Fedeli, Inaki Estella Aguerri, and Dajun Yue

"Comprehensive capacity optimization of the Amazon Transportation Network", 11:50 - 12:15pm, presented by Marc Christian Bataillou Almagro

Room: Bldg A Lvl 4 A403
Re-Inventing Labor Planning at Amazon
October 27, 11:00 AM - 12:15 PM EDT
Website: Link

Session Chair: Martin Savelsbergh

Presentations:
"Associate capacity forecasting", 11:00 - 11:15am, presented by Selin Tosun and Jeronimo Callejas

"Optimizing capacity: Workforce regionalization", 11:15 - 11:30am, presented by Hadi Panahi, Jeronimo Callejas, and Rachel Rutkowski

"Optimizing labor use and availability on the day of operations", 11:30 - 11:45am, presented by Nayeon Kim, Ramon Auad, and Zeynep Sargut

"Conceptualizing labor planning as a dynamic process: Developing a versatile qualitative dataset to support Large Language Models", 11:45 - 12:00pm, presented by Alana Scholl

"Large Language Models for model exploitability: A use-case in labor planning", 12:00 - 12:15pm, presented by Anis Ben Said, Roman Levkin, Thomas Fillebeen, David Dong, and Brian Salisbury

Room: Bldg A Lvl 4 A407
Supply Chain Optimization at Amazon
October 27, 1:15 PM - 2:30 PM EDT
Website: Link

Session Chair: Garrett van Ryzin

Presentations:
"Vendor collaboration using consensus planning", 1:15 - 1:33pm, presented by Garrett van Ryzin

"Controlling Cube Per Package under capacity-demand mismatch", 1:33 - 1:51pm, presented by Harprinderjot Singh, Yufei Wang, and Arun Jotshi

"Multiple Response Agents: Fast, feasible, approximate primal recovery for dual optimization methods", 1:51 - 2:09pm, presented by Tetiana Parshakova, Yicheng Bai, Garrett van Ryzin, and Stephen Boyd

"Multi-objective optimization in assortment planning for speed deliveries", 2:09 - 2:27pm, presented by Ozan Candogan, Lijuan Bitoun, Jonathan Jonker, and Nanjing Jian

Room: Bldg A Lvl 4 A402
Emerging Applications of Optimization
October 27, 1:15 PM - 2:30 PM EDT
Website: Link

Session Chair: Taghi Khaniyev

Presentations:
"Optimizing over Graph Neural Networks with an application to optimal brain tumor resection", 1:30 - 1:45pm, presented by Kaan Cakiroglu, Taghi Khaniyev, Ozlem Karsu, and Sahin Hanalioglu

"Personalized brain parcellation via optimizing topological symmetry", 1:45 - 2:00pm, presented by Taghi Khaniyev, Ugur Sorar, Egemen Gok, Efecan Cekic, Kaan Cakiroglu, and Sahin Hanalioglu

"A metamodel-based general-purpose calibration tool for simulation models", 2:15 - 2:30pm, presented by Elif Sena Isik, Taghi Khaniyev, Turgay Ayer, Jagpreet Chhatwal, and Ismail Fatih Yildirim

Room: Bldg B Lvl 2 B201
Open Energy Modeling
October 27, 1:15 PM - 2:30 PM EDT
Website: Link

Presentations:
"PyPSA on the cloud: Easy on-demand scaling for open energy models", 1:27 - 1:39pm, presented by Satheesh Maheswaran and Soham Garg

Room: Bldg B Lvl 2 B203
Forecasting and Optimization Methods and Models for Last Mile Operations
October 27, 4:15 PM - 5:30 PM EDT
Website: Link

Session Chair: Yaniv Mordecai

Presentations:
"ForeScaling: Time series forecast scaling strategies for mega-models", 4:15 - 4:27pm, presented by Yaniv Mordecai

"Large-scale hierarchical forecast reconciliation", 4:27 - 4:39pm, presented by Chinmoy Mohapatra, Abhilasha Katariya, and Yaniv Mordecai

"AI Agentic work flows", 4:39 - 4:51pm, presented by Arkajyoti Misra

"Jurisdiction models", 4:51 - 5:03pm, presented by Dipal Gupta

"Exploring the AI frontier in optimization research", 5:03 - 5:15pm, presented by Michael Wagner and Rohit Malshe

"Optimization models explained - Optix", 5:15 - 5:27pm, presented by Gokce Kahvecioglu, Jin Ye, and Ling Zhang

Room: Bldg A Lvl 4 A402
Market Design, Platforms, and Matching Markets
October 27, 4:15 PM - 5:30 PM EDT
Website: Link

Presentations:
"Personalization vs. marketplace diversity: How recommendation algorithms shape pricing, participation, and consumer search", 4:51 - 5:09pm, presented by Anil Omer Saritac, Nur Kaynar, and Ozge Sahin

Room: Bldg B Lvl 3 B311
GPU Acceleration of Optimization Algorithms I
October 27, 4:15 PM - 5:30 PM EDT
Website: Link

Session Chair: Matthew Galati

Room: Bldg B Lvl 2 B201
GPU Acceleration of Optimization Algorithms II
October 28, 8:00 AM - 9:15 AM EDT
Website: Link


Session Chair: Matthew Galati



Room: Bldg B Lvl 2 B201
Amazon Last Mile Planning
October 28, 11:00 AM - 12:15 PM EDT
Website: Link

Session Chair: Liron Yedidsion

Presentations:
"Just-in-Time-Aware Long Lead Buying Policy", 11:00 - 11:15am, presented by Yuguang Wu

"Prize collecting traveling salesmen problem, and application of KDTrees for fast computation", 11:15 - 11:30am, presented by Rohit Malshe and Michael Wagner

"Optimizing channel allocation in last mile delivery network", 11:30 - 11:45am, presented by Gokce Kahvecioglu and Jin Ye

"Fleet Allocation and Balancing of branded vehicles, electric vehicles and rentals", 11:45 - 12:00pm, presented by Ram Thiruveedhi, Abhilasha Katariya, Xiaodong Lan, Hugh Medal, and Animesh Biyani

"Multi-period newsvendor for capacity planning", 12:00 - 12:15pm, presented by Liron Yedidsion and Chinmoy Mohapatra

Room: Bldg A Lvl 4 A402
Emerging Topics in Platform Operations and Retail
October 28, 1:15 PM - 2:30 PM EDT
Website: Link

Presentations:
"Assortment optimization, price competition and fairness", 2:15 - 2:30pm, presented by Wentao Lu, Ozge Sahin, and Ruxian Wang

Room: Bldg B Lvl 3 B311
Advanced Analytics
October 28, 2:45 PM - 4:00 PM EDT
Website: Link

Presentations:
"Parametric workload distribution for balanced optimization: Theory and application", 3:39 - 3:48pm, presented by Abhay Singh Bhadoriya

Room: Bldg A Lvl 4 A409
Coordinated Planning in Complex Transport Networks
October 28, 2:45 PM - 4:00 PM EDT
Website: Link

Presentations:
"Dynamic fleet allocation for last-mile delivery networks", 3:48 - 3:57pm, presented by Bhargav Ganguly, John Phelix, Andre Snoeck, Jin Ye, and Daniel Merchan

Room: Bldg A Lvl 3 A314
Supply Chain Applications of Large-Scale Discrete Optimization
October 28, 4:15 PM - 5:30 PM EDT
Website: Link

Session Chairs: Madison Van Dyk and Cristiana Lara

Presentations:
"Algorithms for hub location with network connectivity constraints", 4:15 - 4:30pm, presented by Ozgun Elci

"The single-vehicle routing problem with precedence constraints", 4:30 - 4:45pm, presented by Qie He, Michael Rice, and Renato Werneck

"Large-scale resource allocation: A column generation multi-device GPU based algorithm", 4:45 - 5:00pm, presented by Marcus Poggi, Qi Chen, Ozlem Bilginer, Granville Paules, and Zsolt Csizmadia

"GPU acceleration of first order methods for large scale resource assignment - Benchmark platform and results", 5:00 - 5:15pm, presented by Qi Chen, Ozlem Bilginer, Marcus Poggi, Zsolt Csizmadia, and Granville Paules

Room: Bldg B Lvl 2 B213
Coordination in E-Commerce Supply Chains
October 29, 8:00 AM - 9:15 AM EDT
Website: Link

Presentations:
"Multi-tier capacity planning in Fulfillment Center and Storage Networks at Amazon", 8:27 - 8:36am, presented by Junho Lee and Song Song

Room: Bldg A Lvl 3 A306
Sustainable Supply Chain Management
October 29, 8:00 AM - 9:15 AM EDT
Website: Link

Presentations:
"Analyzing the impact of sustainability drivers in Amazon's supply chain management: A DEMATEL approach", 8:09 - 8:18am, presented by Sayan Banerjee

Room: Bldg B Lvl 3 B304
Emerging Topics in Responsible and Sustainable Operations
October 29, 8:00 AM - 9:15 AM EDT
Website: Link

Presentations:
"Electric vehicle charging station access sharing and economics implication", 8:45 - 9:00am, presented by Yuan Ma, Roman Kapuscinski, and Ozge Sahin

Room: Bldg B Lvl 3 B312
Location and Routing
October 29, 9:30 AM - 10:45 AM EDT
Website: Link

Presentations:
"Evaluating innovation adoption factors in Amazon's last mile delivery: A DEMATEL study", 9:48 - 9:57am, presented by Sayan Banerjee

"Charge planning for a dedicated electric delivery fleet", 10:24 - 10:33am, presented by Rami Sleiman, Andre Snoeck, and Arun Akkinepally

Room: Bldg A Lvl 4 A401
Transportation Systems
October 29, 9:30 AM - 10:45 AM EDT
Website: Link

Presentations:
"Transforming Amazon's last-mile delivery with optimization-based route allocation", 10:24 - 10:33am, presented by Ling Zhang, Gokce Kahvecioglu, and Jin Ye

Room: Bldg A Lvl 4 A411
Logistics and Distribution Networks
October 29, 9:30 AM - 10:45 AM EDT
Website: Link

Presentations:
"Optimization-based consolidation engine for last mile delivery", 10:06 - 10:15am, presented by Hao Zhou

"AwESM: Informing energy-aware last mile strategic network design", 10:15 - 10:24am, presented by Harry Birnbaum, Andre Snoeck, Nic Wayand, and Daniel Merchan

Room: Bldg A Lvl 4 A404
AI in Financial and Technical Applications
October 29, 9:30 AM - 10:45 AM EDT
Website: Link

Presentations:
"INSPIRE: INtelligent Safety and Performance Interior Cabin Rating Engine", 10:24 - 10:33am, presented by Girik Malik and Jin Ye

Room: Bldg B Lvl 4 B403
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-art algorithms to solve real world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team. The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high‐judgement decision‐making where we cannot apply automation. Key job responsibilities Use statistical and machine learning techniques to create scalable risk management systems Analyzing and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches.
US, NY, New York
Are you passionate about conducting research to develop and grow leaders? Would you like to impact more than 1M Amazonians globally and improve the employee experience? If so, you should consider joining the People eXperience & Technology Central Science (PXTCS) team. Our goal is to be best and most diverse workforce in the world. PXTCS uses science, research, and technology to optimize employee experience and performance across the full employee lifecycle, from first contact through exit. We use economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. This individual should be skilled in core data science tools and methods, icnluding SQL, a statistical software package (e.g., R, Python, or Stata), inferential statistics, and proficient in machine learning. This person should also have strong business acumen to navigate complex, ambiguous business challenges — they should be adept at asking the right questions, knowing what methodologies to use (and why), efficiently analyzing massive datasets, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders). In order to move quickly, deliver high-quality results, and adapt to ever-evolving business priorities, effective communication skills in research fundamentals (e.g., research design, measurement, statistics) will also be a must. Major responsibilities will include: - Managing the full life cycle of large-scale research initiatives across multiple business segments that impact leaders in our organization (i.e., develop strategy, gather requirements, manage, and execute) - Serving as a subject matter expert on a wide variety of topics related to research design, measurement, analysis - Working with internal partners and external stakeholders to evaluate research initiatives that provide bottom-line ROI and incremental improvements over time - Collaborating with a cross-functional team that has expertise in social science, machine learning, econometrics, psychometrics, natural language processing, forecasting, optimization, business intelligence, analytics, and policy evaluation - Ability to query and clean complex datasets from multiple sources, to funnel into advanced statistical analysis - Writing high-quality, evidence-based documents that help provide insights to business leaders and gain buy-in - Sharing knowledge, advocating for innovative solutions, and mentoring others Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 1M employees across hundreds of chapters around the world. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust. Flexibility It isn’t about which hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We offer flexibility and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth We care about your career growth, too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it. About the team We are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.
US, WA, Bellevue
The Mission of Amazon's Artificial General Intelligence (AGI) team is to "Build world-class general-purpose intelligence services that benefits every Amazon business and humanity." Are you a data enthusiast? Are you a creative big thinker who is passionate about using data to direct decision making and solve complex and large-scale challenges? If so, then this position is for you! We are looking for a motivated individual with strong analytical and communication skills to join us. In this role, you will apply advanced analytics techniques, AI/ML, and statistical concepts to derive insights from massive datasets. The ideal candidate should have expertise in AI/ML, statistical analysis, and the ability to write code for building models and pipelines to automate data and analytics processing. They will help us design experiments, build models, and develop appropriate metrics to deeply understand the strengths and weaknesses of our systems. They will build dashboards to automate data collection and reporting of relevant data streams, providing leadership and stakeholders with transparency into our system's performance. They will turn their findings into actions by writing detailed reports and providing recommendations on where we should focus our efforts to have the largest customer impact. A successful candidate should be a self-starter, comfortable with ambiguity with strong attention to detail, and have the ability to work in a fast-paced and ever-changing environment. They will also help coach/mentor junior scientists in the team. The ideal candidate should possess excellent verbal and written communication skills, capable of effectively communicating results and insights to both technical and non-technical audiences
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist to work on methodologies for Generative Artificial Intelligence (GenAI) models. As an Applied Scientist, you will be responsible for supporting the development of novel algorithms and modeling techniques to advance the state of the art. Your work will directly impact our customers and will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multi-modal Large Language Models (LLMs) and GenAI. You will have significant influence on our overall strategy by working at the intersection of engineering and applied science to scale pre-training and post-training workflows and build efficient models. You will support the system architecture and the best practices that enable a quality infrastructure. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Pre-training and post-training multimodal LLMs - Scale training, optimization methods, and learning objectives - Utilize, build, and extend upon industry-leading frameworks - Work with other team members to investigate design approaches, prototype new technology, scientific techniques and evaluate technical feasibility - Deliver results independently in a self-organizing Agile environment while constantly embracing and adapting new scientific advances About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Principal Applied Scientist with a strong deep learning background, to lead the development of industry-leading technology with multimodal systems. As a Principal Applied Scientist, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modeling, verification and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will be technically strong and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).
US, NY, New York
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. Key job responsibilities - Lead and execute complex, ambiguous research projects from ideation to production deployment - Drive technical strategy and roadmap decisions for ML/AI initiatives - Collaborate cross-functionally with product, engineering, and business teams to translate research into scalable products - Publish research findings at top-tier conferences and contribute to the broader scientific community - Establish best practices for ML experimentation, evaluation, and deployment
US, NY, New York
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. Key job responsibilities - Lead and execute complex, ambiguous research projects from ideation to production deployment - Drive technical strategy and roadmap decisions for ML/AI initiatives - Collaborate cross-functionally with product, engineering, and business teams to translate research into scalable products - Publish research findings at top-tier conferences and contribute to the broader scientific community - Establish best practices for ML experimentation, evaluation, and deployment
US, CA, Palo Alto
About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through 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. About our team SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Applied Scientist with machine learning engineering background who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine learning systems. We are looking for a talented Applied Scientist with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide. Key job responsibilities As a Sr Applied Scientist, you will: * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business. * Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems. * Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.
US, CA, Sunnyvale
As a Principal Applied Scientist within the Artificial General Intelligence (AGI) organization, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions, set the standard for scientific excellence, and make decisions that affect the way we build and integrate algorithms. A Principal Applied Scientist will solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader; develop solutions that are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility; and tackle intrinsically hard problems acquiring expertise as needed. Principal Applied Scientists are expected to decompose complex problems into straightforward solutions. You will amplify your impact by leading scientific reviews within your organization or at your location; and scrutinize and review experimental design, modeling, verification and other research procedures. You will also probe assumptions, illuminate pitfalls, and foster shared understanding; align teams toward coherent strategies; and educate keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. AGI Principal Applied Scientists help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. You will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities You will be responsible for defining key research directions, inventing new machine learning techniques, conducting rigorous experiments, and ensuring that research is translated into practice. You will also develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. A Principal Applied Scientist will participate in organizational planning, hiring, mentorship and leadership development. You will build scalable science and engineering solutions, and serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).
US, CA, Sunnyvale
Our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc. Your work will directly impact our customers in the form of novel products and services .