Build on Trainium is sponsored by Amazon Web Services (AWS). This credit program focuses on AI research and university education to support innovation and development on AWS Trainium, which are chips purpose-built for high-performance deep learning training of generative AI models.

Build on Trainium: Kernels for ML Acceleration call for proposals — Spring 2026 - Amazon Science Information and knowledge management Operations research and optimization Security, privacy, and abuse prevention Our scientific contributions Research from our scientists and collaborators. Our experts present and discuss cutting-edge research at scientific meetings globally.

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Faculty research opportunities on industry-scale technical challenges. Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Faculty research opportunities on industry-scale technical challenges.

Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Build on Trainium: Kernels for ML Acceleration call for proposals — Spring 2026 Building the future of AI with AWS Trainium By Amazon Research Awards team What is Build on Trainium?

Build on Trainium is a $110MM credit program focused on AI research and university education to support the next generation of innovation and development on AWS Trainium . AWS Trainium chips are purpose-built for high-performance deep learning (DL) training of generative AI models, including large language models (LLMs) and latent diffusion models.

Build on Trainium provides compute credits to novel AI research on Trainium, investing in leading academic teams to build innovations in critical areas including new model architectures, ML libraries, optimizations, large-scale distributed systems, and more.

This multi-year initiative lays the foundation for the future of AI by inspiring the academic community to utilize, invest in, and contribute to the open-source community around Trainium. Combining these benefits with Neuron software development kit (SDK) and recent launch of the Neuron Kernel Interface (NKI) , AI researchers can innovate at scale in the cloud. What are AWS Trainium and Neuron?

AWS Trainium is an AI chip developed by AWS for accelerating building and deploying machine learning models. Built on a specialized architecture designed for deep learning, Trainium accelerates the training and inference of complex models with high output and scalability, making it ideal for academic researchers looking to optimize performance and costs.

This architecture also emphasizes sustainability through energy-efficient design, reducing environmental impact. Amazon has established a dedicated Trainium research cluster featuring up to 40,000 Trainium chips, accessible via Amazon EC2 Trn1 instances. These instances are connected through a non-blocking, petabit-scale network using Amazon EC2 UltraClusters, enabling seamless high-performance ML training.

The Trn1 instance family is optimized to deliver substantial compute power for cutting-edge AI research and development. This unique offering not only enhances the efficiency and affordability of model training but also presents academic researchers with opportunities to publish new papers on underrepresented compute architectures, thus advancing the field.

Focus on Kernels for ML Acceleration GenAI for Kernel Development : As kernel development becomes increasingly complex and specialized for accelerators like Trainium, generative AI offers opportunities to automate and optimize the kernel development lifecycle.

We seek proposals that leverage GenAI to accelerate kernel creation, optimization, and maintenance on Trainium, including: Automated Kernel Generation : Methods for using GenAI to generate high-performance NKI kernels from high-level specifications, including natural language descriptions, mathematical formulations, or reference implementations in other frameworks.

This includes agentic workflows leveraging reinforcement learning, inference-time compute scaling, and multi-agent systems with iterative refinement where agents execute kernels, observe performance metrics, and progressively improve implementations through feedback loops.

Kernel Optimization and Tuning : Techniques for using GenAI to automatically optimize existing kernels, including instruction scheduling, memory access patterns, tile size selection, and register allocation strategies.

This encompasses knowledge distillation and memory systems that capture, distill, and organize optimization insights into structured knowledge bases of architecture-specific heuristics, enabling continuous learning from both successful and failed optimization attempts.

Performance Debugging and Analysis : AI-assisted tools for identifying performance bottlenecks, suggesting optimizations, and explaining performance characteristics of NKI kernels.

This includes methods for correctness verification and robustness testing that rigorously verify functional correctness beyond standard test cases, detecting subtle bugs and reward hacking behaviors where kernels achieve favorable metrics while producing incorrect outputs.

Code Completion and Synthesis : Methods for intelligent code completion, pattern recognition, and synthesis of common kernel idioms specific to NKI and Trainium architecture.

This includes transfer learning and domain adaptation techniques for adapting kernel generation across different hardware generations or compiler versions with minimal training data, as well as explain ability methods that make AI-generated kernels interpretable and maintainable through documentation generation and collaborative human-AI development workflows.

Benchmark Construction and Evaluation : Development of comprehensive, representative benchmark suites for evaluating kernel generation and optimization techniques on Trainium, including systematic methodologies for creating diverse kernel collections spanning operator types, tensor shapes, data layouts, and compute/memory-bound characteristics representative of real-world model workloads.

Developer Tools and Profiling: Effective kernel development requires sophisticated tooling for understanding performance, debugging behavior, and iterating designs.

We seek proposals that advance the NKI developer experience on Trainium, including: Novel Profiling Visualizations and Human-Computer Interaction : Innovative visualization techniques that blend performance analysis with HCI research to make complex kernel behavior intuitive and actionable, including interactive 3D performance landscapes, temporal execution flow visualizations, comparative visual analytics across kernel variants, attention-driven bottleneck highlighting, and multi-dimensional performance space exploration tools that enable developers to quickly identify optimization opportunities through visual pattern recognition.

Performance Modeling and Estimation : Advanced methods for predicting kernel performance before execution, including analytical roofline models extended for Trainium architecture, learned performance predictors using neural networks trained on kernel characteristics, hybrid symbolic-numeric performance models, static analysis techniques for estimating memory bandwidth and compute utilization, and probabilistic performance bounds that account for hardware variability and dynamic effects.

Debugging and Verification Tools : Methods for validating kernel correctness, detecting numerical issues, and debugging complex kernel behaviors, including symbolic execution and formal verification approaches.

Interactive Development Environments : Enhanced IDE support for NKI development, including syntax highlighting, type checking, inline performance hints derived from real-time estimation models, and integration with existing development workflows. Kernel Porting and Cross-Framework Translation: The ecosystem of kernel languages continues to fragment, creating barriers to adoption and limiting code reuse.

We seek proposals that enable seamless translation between kernel frameworks while preserving high performance on Trainium, including: Automated Kernel Translation : Methods for automatically porting kernels from other frameworks (Triton, CUDA,CuTe, Pallas) to NKI specifically while maintaining or improving performance, including semantic-preserving transformations and architecture-specific optimizations Cross-Framework Optimization : Methods for leveraging optimization techniques across different kernel languages, including pattern matching, optimization transfer learning, and unified intermediate representations.

Performance Portability : Approaches for ensuring translated kernels achieve competitive performance with hand-written implementations, including auto-tuning, architecture-aware code generation, and performance validation frameworks. Kernel Language Design and Abstractions: The design of kernel languages fundamentally shapes developer productivity and achievable performance.

We seek proposals that explore novel language representations, APIs, and abstractions for NKI on Trainium, including: Alternative Language Representations : Novel representations for expressing kernel computations, including tensor comprehensions, polyhedral models, and domain-specific languages that improve expressiveness or enable better optimization.

API Design and Primitives : Improved APIs and primitive operations for kernels including higher-level abstractions that maintain performance while improving usability, composability, and maintainability. Abstraction Layers : Methods for building layered abstractions that allow developers to work at different levels of detail, from high-level operations to low-level hardware control, with smooth transitions between levels.

Submission period: March 25 — May 6, 2026 (11:59 PM Pacific Time) Decision letters will be sent out in August 2026.

Selected Principal Investigators (PIs) may receive the following: Applicants are encouraged to request AWS Promotional Credits in one of two ranges: AWS Promotional Credits, up to $50,000 AWS Promotional Credits, up to $250,000 and beyond AWS Trainium training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers Awards are structured as one-time unrestricted gifts.

The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel. Your receipt and use of AWS Promotional Credits is governed by the AWS Promotional Credit Terms and Conditions , which may be updated by AWS from time to time.

Please refer to the ARA Program rules on the Rules and Eligibility page. PIs are encouraged to exemplify how their proposed techniques or research studies advance kernel optimization, LLM innovation, distributed systems, or developer efficiency. PIs should either include plans for open source contributions or state that they do not plan to make any open source contributions (data or code) under the proposed effort.

Proposals for this CFP should be prepared according to the proposal template and are encouraged to be a maximum of 3 pages, not including Appendices.

Proposals will be evaluated on the following: Creativity and quality of the scientific content Potential impact to the research community and society at large Interest expressed in open-sourcing model artifacts, datasets and development frameworks Intention to use and explore novel hardware for AI/ML, primarily AWS Trainium and Inferentia Expectations from recipients Amazon Research Awards team Amazon Research Awards recipients announced Amazon Research Awards team Awardees, who represent 41 universities in 8 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

AI-native 6G: From networks to intelligence fabrics Imen Grida Ben Yahya , Ejaz Sial , Kaniz Mahdi “Network language models” will coordinate complex interactions among intelligent components, computational infrastructure, access points, data centers, and more.

Isabelle/HOL: The proof assistant behind the Nitro Isolation Engine Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.

Quantum Applied Scientist, Processor Test & Measurement, Amazon Center for Quantum Computing The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire a Quantum Research Scientist in the Processor Test and Measurement group.

You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have a deep and broad knowledge of experimental measurement techniques. Candidates with a track record of original scientific contributions will be preferred.

We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. As a research scientist you will be expected to work on new ideas and stay abreast of the field of experimental quantum computation.

Key job responsibilities We are looking to hire a Research Scientist to develop and test novel calibration and optimization tools for Quantum Error Correction on large scale quantum processors. You will be on a team of engineers and scientists at the frontier of quantum processor control and error correction. You are expected to take part in high-impact research projects that intersect with our engineering roadmap.

We are looking for candidates with strong engineering principles and resourcefulness. Organization and communication skills are essential. A day in the life About the team Why AWS?

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry.

As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Inclusive Team Culture AWS values curiosity and connection.

Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.

Diverse Experiences AWS 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.

Mentorship & 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, mentorship 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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.

Applied Scientist, WW Promotions Science Amazon's Worldwide Pricing & Promotions organization is seeking a strong Applied Scientist to help solve complex business problems involving promotional strategies at a global scale. This Applied Scientist will operate in a team of other scientists and economists.

Our team applies causal inference, statistics, machine learning, forecasting, optimization, economics, and experimentation to drive actionable insights and to improve strategic business decision-making. This is an individual contributor role that requires collaboration across teams and functions to solve core business problems for the company around setting promotional strategies.

The work is part of significant scientific investments in promotions intelligence systems that forecast customer demand and optimize promotions strategies across different surfaces.

Key job responsibilities * Invent or adapt new scientific approaches, models, or algorithms inspired and driven by customers' needs and benefits * Produce research papers and reports that have the same level of correctness, scholarship, usefulness, completeness, depth, rigor, and originality as a top-tier external publication * Implement solutions that will be deployed into production or directly support production systems * Write clear, useful documentation describing algorithms and design choices in your components to make it possible for others to understand and reproduce your work * Contribute to operational excellence in the team's deliverable * Analyze the performance of your methods and models to understand the gaps, and iteratively propose solutions to improve * Champion the adoption of scientific advancements in the team * Help new teammates ramp up and understand who our customers are, what their needs are, how the team's solutions work, and how scientific components fit in those solutions A day in the life As an Applied Scientist on the WW Promotions Science team, you invent or adapt new scientific approaches, models, or algorithms to solve real-world business problems.

Your work uses the latest (or the most appropriate) techniques from academic literature. You work semi-autonomously to successfully deliver solutions that are consistently of high quality (efficient, reproducible, testable code). You work collaboratively with teammates, partners, and stakeholders.

You recognize discordant views and take part in constructive dialogue to resolve them. You adopt and identify opportunities to refine mechanisms to raise the general scientific knowledge in the team. About the team The WW Promotions Science team is responsible for driving scientific innovation to support pricing and promotions programs across Amazon's businesses.

We specialize in experimental and observational causal methods, forecasting, and optimization. We apply these tools to drive business decision making at scale, leading to launch decisions of new pricing algorithms and new promotion strategies, understanding short- and long-term value of different programs, and the prioritization of budget allocations.

We also develop models to set optimal prices and promotions, and define innovative price guardrails and incentives to optimize for long-term program health. Sr. Applied Scientist , Mobile Manipulation Robotics (I/O) About the Role Amazon Robotics is transforming warehouse automation through edge AI and machine learning applied to real-world robotics challenges.

We're seeking a Senior Applied Scientist to advance our mobile manipulation capabilities by developing learning-based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments. This role offers the opportunity to apply state-of-the-art research to production systems operating at Amazon's unprecedented scale.

What You'll Do As a Senior Applied Scientist, you'll develop and deploy machine learning models that enable mobile manipulators to perform complex tasks autonomously. You'll work at the intersection of perception, learning, and control to create intelligent systems that can adapt to diverse warehouse scenarios with minimal task-specific programming.

Key job responsibilities • Design, develop, train, and deploy deep learning models for perception tasks (e.g., object detection, segmentation, pose estimation, tracking) • Develop and maintain robust camera calibration pipelines (intrinsic, extrinsic, hand-eye calibration, multi-camera systems) • Build perception systems for robotic manipulation including grasp detection, object pose estimation, and visual servoing • Improve model performance through architecture optimization, data curation, and training strategies • Build and maintain production-quality perception codebases with proper testing and documentation • Profile and optimize models for real-time inference on embedded/edge platforms • Collaborate with cross-functional teams (robotics, motion planning, controls) to integrate perception outputs for manipulation tasks • Establish best practices for model versioning, experiment tracking, and MLOps • Mentor junior engineers and contribute to technical roadmap planning A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children.

Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2.

Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you!

At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Are you inspired by invention?

Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics.

We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day.

We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun Sr. Applied Scientist, Amazon Publisher Monetization - Ads Advertising is a complex, multi-sided market with many technologies at play within the industry.

The industry is rapidly growing and evolving as viewers are shifting from traditional TV viewing to streaming video and publishers are increasingly adding video content to their online experiences. Amazon’s video advertising is a rising competitor in this industry.

Amazon’s service has differentiated assets in our customer & audience insights, exclusive video content, and associated inventory that position us well as an end-to-end service for advertisers and agencies. We are innovating at the intersection of advertising, e-commerce, and entertainment.

Amazon Publisher Monetization (APM) is looking for a a passionate and experienced scientist who is adept at a variety of skills; especially in generative AI, computer vision, and large language models that will accelerate our plans to maximize yield via AI-driven contextual targeting, Ads syndication and more.

The ideal candidate will be an inventor at heart, they will provide science expertise, rapidly prototype, iterate, and launch, foster the spirit of collaboration and innovation within our larger sister teams and their scientists, and execute against a compelling product roadmap designed to bring AI-led science innovation to solve one of the most challenging problems in advertising.

Key job responsibilities This role is focused on shaping our approach to the solving the trifecta of advertising - serving the right ad to the right viewer at the right moment - delivering engaging ads for viewers, improved performance for advertisers, and maximizing the yield of our supply inventory.

Responsibilities include: * Partner deeply with Product and Engineering to develop AI-based solutions to generating contextual signals across both video (VOD and Live) and display ads. * Drive end-to-end applied science projects that have a high degree of ambiguity, scale, complexity. * Provide technical/science leadership related to computer vision, large language models and contextual targeting.

* Research new and innovative machine learning approaches. * Partner with Applied Scientists across the broader org to make the most of prior art and contribute back to this community the innovation that you come up with.

Applied Scientist II, Alexa International Alexa International is looking for passionate, talented, and inventive Senior Applied Scientists to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge.

Senior applied scientists will drive cross-team scientific strategy, influence partner teams, and deliver solutions that have broad impact across Alexa's international products and services.

Key job responsibilities As a Applied Scientist with II the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs, particularly delivering industry-leading scientific research and applied AI for multi-lingual applications — a challenging area for the industry globally.

Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains.

The ideal candidate possesses a solid understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field.

They thrive in fast-paced environment, like to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and are able to influence and align multiple teams around a shared scientific vision. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data.

* Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using advanced and innovative techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer’s location and language. * Quickly experiment and set up experimentation framework for agile model and data analysis or A/B testing.

* Contribute through industry-first research to drive innovation forward. * Drive cross-team scientific strategy and influence partner teams on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems.

* Lead end-to-end delivery of scientifically complex solutions from research to production, including reusable science components and services that resolve architecture deficiencies across teams. * Serve as a scientific thought leader, communicating solutions clearly to partners, stakeholders, and senior leadership.

* Actively mentor junior scientists and contribute to the broader internal and external scientific community through publications and community engagement. Principal Applied Scientist, PXT About the Role In this role, you will own the science strategy and technical vision for this intelligence layer, leading a team of applied scientists working across GenAI and predictive modeling.

You will shape how heterogeneous signals — text, behavioral, network, temporal — come together to power talent applications at Amazon scale, from workforce forecasting to personalized development to compensation strategy.

You will identify opportunities where science investment can have material impact on long-term objectives or annual goals and build consensus around needed investments, working comfortably across different modeling paradigms and data modalities to guide principal and senior scientists in their most challenging and strategic decisions while serving as the strategic science advisor to PXT leaders operating at the Director, VP, and SVP levels.

As a hands-on leader, you will personally own development and delivery of the most complex science problems at the intersection of multiple ML disciplines, stay current with emergent AI/ML science and engineering trends to influence focus areas in a rapidly evolving landscape, and participate in organizational planning, hiring, mentorship, and leadership development.

Key job responsibilities • Lead technical initiatives in people science models, driving breakthrough approaches through hands-on research and development in areas like foundation models for predictive modeling, efficient multi-modal LLMs, and zero-shot learning • Design and implement novel ML architectures that push the boundaries of how workforce signals are represented, fused, and predicted at scale • Guide technical direction for research initiatives across the team, ensuring robust performance in production environments serving hundreds of thousands of employees • Mentor and develop senior scientists while maintaining strong individual technical contributions on the most complex cross-domain problems • Collaborate with engineering teams to optimize and scale models for real-world talent applications • Influence technical decisions and implementation strategies across teams, shaping the long-term platform architecture About the team The People eXperience and Technology (PXT) Core Science Team uses science, engineering, and customer-obsessed problem solving to proactively identify mechanisms, process improvements, and products that simultaneously improve Amazon and Amazonians' lives, wellbeing, and value of work.

As an interdisciplinary team combining talents from machine learning, statistics, economics, behavioral science, engineering, and product development, the Core Science team develops and delivers measurable solutions through innovation and rapid prototyping to accelerate informed, accurate, and reliable decision-making backed by science and data.

Applied Scientist II, Reinforcement Learning Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale.

We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments.

This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models.

At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence.

The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration.

Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists Applied Scientist II, Last Mile Science Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast?

Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner.

We are looking for an enthusiastic, customer obsessed, Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization.

This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon.

Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes.

This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment.

Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics