Careers

AWS Support includes experts from across AWS who help our customers design, build, operate, and secure their cloud environments. You’ll join a diverse team of technical experts in dozens of countries who help customers achieve more with the AWS cloud. Do you have proven analytical capabilities to identify business opportunities, develop advanced ML solution, Gen AI products, predictive models and optimization algorithms to help us build state of the art Support organization? Team AiRES (Analytics, Insights, Research, Engineering and Science) is looking for a passionate Applied Scientist to deliver insights and identify opportunities through the use of statistics, machine learning, and deep learning to drive business and operational improvements. A successful candidate must be passionate about building solutions that will help drive a more efficient operations network and optimize cost. In this role, you will partner with data engineering, Tooling team, operations, Training, Customer Service, Capacity planning and finance teams, driving optimization and prediction solutions across the network. At Amazon, we are working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. We set big goals and are looking for people who can help us reach and exceed them. Amazon Web Services (AWS) is one of the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Amazon Web Services, Inc. provides services for broad range of applications including compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), security, and application development, deployment, and management. Key job responsibilities Team AiRES is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong machine learning background who will be responsible for establishing scalable, efficient, automated processes for carrying out large-scale data analysis, machine-learning model development, model validation and serving customers. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). As an Senior Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization and will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with and launching new features, iterating models & stay up to date on latest enhancements in the Data Science ecosystem. A day in the life You will collaborate closely with other Data Scientists and leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development of multimodal Large Language Models and Generative Artificial Intelligence solutions and will own the end to end development & deployment at scale of Generative AI and ML solutions developed by the team. Prior experience in Advanced Applied science & Engineering proficiency in deploying models & optimizing for the best optimal solution will be mandatory. About the team Diverse Experiences: AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job below, 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 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.

Building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. Key job responsibilities 1. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 2. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. 3. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 4 Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 5. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing.

How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promises across hundreds of millions of packages daily? How does it decide how many trucks and how much labor are required to ship orders across the network? SCOT Fulfillment Optimization (FO) owns the optimization and forecasting science behind these decisions. We are seeking Applied Scientists to join the FO Science & Tech team in Barcelona (alternatively: Luxembourg or London) with a strong academic background in optimization, machine learning, and/or time-series forecasting. • You will design and build state-of-the-art machine learning and optimization models that power Amazon's fulfillment decisions at an unprecedented scale across two core scientific pillars: • Large-Scale Optimization and Planning: Designing planning systems for order assignment and resource utilization, while balancing multi-objective cost-speed tradeoffs to enable controllers to steer millions of shipments per hour optimally. • Demand Forecasting & Predictive ML: Developing time-series forecasts for customer demand, incorporating contextual information (weather, sales, order properties), and modeling uncertainty for core planning systems. Basic qualifications • PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience) • Strong programming skills (Python preferred; experience with optimization solvers a plus) • Research experience in one or more: • Large-scale mathematical programming (LP, MIP, decomposition methods) • Combinatorial optimization (assignment, scheduling, network flows) • Multi-objective optimization and control • Large-scale time-series forecasting (GenAI models, probabilistic forecasting, uncertainty quantification) • Causal inference (spatiotemporal causal modeling, offline policy evaluation) Preferred qualifications • Experience building optimization systems that run in production at scale • Being comfortable with ambiguity and fast iteration cycles • Publications in relevant venues Key job responsibilities Design and implement optimization and forecasting models for large-scale fulfillment problems, from order assignment to network flow control. Build research prototypes end-to-end: from problem formulation through scalable implementation to production validation. Analyse complex tradeoffs (cost, speed, capacity, accuracy) and translate findings into actionable recommendations for leadership and operations teams. Collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily. A day in the life You formulate an optimization or forecasting problem on a whiteboard with teammates, then prototype it in Python with real data by the afternoon. You run experiments against production-scale datasets, iterate on the model, and present results to stakeholders who will use them to make network decisions next week. Some days you dive deep into solver performance; other days you're explaining a Pareto frontier to an operations leader. You collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers. Alongside fast-turnaround prototypes, you own long-term research bets, the kind that reshape how Amazon's fulfillment network operates at scale. Your work goes live. About the team SCOT Fulfillment Optimization Science & Tech (FO SnT) is the applied research team behind Amazon's fulfillment decision-making systems. We decide how orders get assigned to warehouses, how capacity is allocated across the network, and how cost and speed tradeoffs are managed in real time, at global scale. Our models influence billions of euros in annual operational spend. They protect sites from overload during peak, reduce transportation costs and CO2 emissions, and ensure customers receive their packages when promised. Leadership relies on our science to make investment decisions worth hundreds of millions. We are practitioners of large-scale optimization: MIP formulations, decomposition methods, approximation algorithms, and parallelisation. We use machine learning where it sharpens our decisions, including forecasting, learned heuristics, and multi-armed bandits. We pick the right tool for the problem, not the fashionable one. You will work alongside Senior and Principal scientists, and collaborate with Amazon Scholars and academic partners who bring frontier research into our applied problems. We code our prototypes to be production-ready and collaborate with large engineering teams to ship systems, not papers. Above all, we have fun solving hard real-world problems at real-world speed, failing, learning, and shipping along the way.

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 - 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

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE As a Senior Applied Scientist, you will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including Machine Learning (ML), Artificial Intelligence (AI) and Generative AI, Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems. ABOUT YOU Your work will focus on inventing and extending scientific approaches, models, and algorithms driven by customer needs at the product level, framing new research problems even when the problem is ill-defined and no textbook solution exists. You will lead the design, implementation, and delivery of scientifically complex, end-to-end solutions that are deployed into production, defining system-level requirements and writing a significant portion of the critical-path code. You will develop reusable science components and services that resolve architecture deficiencies and customers’ pain points, while making technical trade-offs for long-term/short-term. You will work independently with limited guidance, and your decision-making will consistently incorporate robust, data-driven business and technical judgment. You will drive your team’s scientific agenda, author internal or external peer-reviewed publications that validate the novelty of your work, mentor and develop other scientists, and build consensus across multiple teams. You will have the opportunity to innovate, invent, and think big, and influence the experiences of millions of customers. We are looking for a results-oriented Senior Applied Scientist with deep expertise in ML, NLP, Deep Learning, GenAI, and/or large-scale distributed computation. As an Applied Scientist, you will... - Understand complex, ambiguous use cases across the business and adopt/extend/design/invent solutions/models that are scalable, efficient, and automated, where neither the problem nor the solution is well defined - Work closely with fellow scientists and software engineers (at Audible and Amazon) to build and productionize models, and deliver novel and highly impactful features - Review models of peers for the purpose of reducing and managing risk to the business, while improving customer experience - Lead the design, development, and production deployment of scientifically complex, end-to-end solutions for Content Understanding, Recommendations, and GenAI-based product features, defining system-level requirements - Drive and lead initiatives that employ the most recent advances in ML/AI/GenAI, drive your team’s scientific agenda, and author peer-reviewed publications - Mentor and grow scientists on the team and across Amazon, and push the boundary of innovation ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

We are seeking Applied and Research Scientists to join the Safe Autonomy Frontiers (SAF) Lab. In this role, you will develop and deploy the science of safe autonomy on highly dynamic robots, advancing the frontier through both foundational research and realization on hardware. You will contribute to achieving safe autonomy across three key areas: control barrier function (CBF) theory for robust and performant safety on hardware; safe reinforcement learning for agile whole-body control; and layered safety filters that interface with learning, perception, and semantic reasoning systems. You will validate your work experimentally on state-of-the-art robotic platforms — removing bottlenecks to deployment and enabling robots to safely operate around humans. You will work with the inventor of CBFs, as well as top scientists and engineers at Amazon developing the next generation of safe autonomy. Key job responsibilities -Advance the science of safe autonomy from formal foundations to the integration with learning and perception to validation on hardware — with particular emphasis on methods that bridge these domains. -Test and validate hardware, with a focus on next generation robotic systems. Including locomotion, manipulation and loco-manipulation. -Leverage the deployments on hardware to validate the underlying science, identifying gaps between theory and practice that drive the next cycle of research. hardware to validate the underlying science, identifying gaps between theory and practice that drive the next cycle of research. -Publish research at top-tier robotics, control, and ML venues, and contribute to Amazon’s scientific reputation in advanced robotics. -Collaborate with SAF Lab and Amazon production teams to move research into robots deployed at Amazon scale. 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: -Medical, Dental, and Vision Coverage -Maternity and Parental Leave Options -Paid Time Off (PTO) -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 The Safe Autonomy Frontiers (SAF) Lab is the first industry research lab dedicated to safe autonomy, founded by the inventor of control barrier functions. We are developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, quadrupeds, and humanoids. Here you will advance performant safety for highly dynamic robots — CBF theory integrated with perception and learning, evaluated on next-generation platforms — and your work will underpin robots operating alongside people at Amazon’s unprecedented scale.

Do you want to shape the next generation of AI-driven customer experiences for Amazon’s most ground-breaking products? Join us and help invent the future of shopping. We are seeking a passionate, innovative, and highly skilled Applied Scientist with expertise in AI, Agentic LLMs, Generative AI, Machine Learning, and NLP to help build LLM-powered solutions for Amazon’s BuyForMe product, which enables Amazon customers to discover and purchase products from any merchants through browser use agents. Our team develops science and agentic AI capabilities that power a seamless, end-to-end shopping experience for Amazon customers. We build and advance technologies such as web agent frameworks, LLM fine-tuning, reinforcement learning, context engineering, RAG, MCP, and automated benchmarking to improve pre-purchase, in-purchase, and post-purchase workflows. Key job responsibilities As an Applied Scientist on our team, you will: Invent, implement, and evaluate state-of-the-art models and agentic systems that directly impact customer experience. Conduct research that may lead to publications, patents, or cross-Amazon technical influence. Collaborate with engineers, product managers, and other scientists to translate business challenges into scalable science solutions. Run experiments with real customer data and validate hypotheses in a high-impact product environment. Drive excellence in model performance, safety, reliability, and evaluation frameworks.

Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people who are passionate about making a meaningful impact on communities and the environment while helping shape the future of sustainable business practices. The Worldwide Sustainability (WWS) organization capitalizes on Amazon's scale and speed to build a more resilient and sustainable company. We manage our social and environmental impacts globally and drive solutions that enable our customers, businesses, and the world to become more sustainable. Through innovative programs and strategic partnerships, we're creating lasting positive change in the communities where we operate while advancing Amazon's commitment to environmental stewardship and social responsibility. We are looking for a robotics scientist to build and operate the first autonomous materials discovery laboratory at Amazon. This role combines deep robotics expertise (motion planning, control, platform integration) with modern Physical AI approaches (vision-language-action models, sim-to-real transfer, agentic orchestration). You will design autonomous experimental workflows that integrate dexterous robotic platforms, analytical instruments, and AI-driven hypothesis generation into a closed-loop discovery pipeline — where foundation models drive hypothesis generation and experimental planning, validated on real hardware under real chemistry. This is not a pure research role. You will work directly with physical robots, laboratory instruments, and deployment pipelines. The work is expected to be published, but the primary measure of success is a working autonomous platform that generates scientific results. Materials science expertise is not required — the team includes domain scientists. What matters is strong AI and robotics foundations, scientific curiosity, and the drive to ship. Key job responsibilities - Develop, train, and benchmark robotic manipulation policies for materials synthesis and characterization using modern policy architectures (VLA architectures, diffusion policies). - Design and execute sim-to-real transfer strategies including domain randomization, physics parameter tuning, and visual domain adaptation for laboratory robotic systems. - Integrate robotic platforms and laboratory instruments into automated workflows via APIs (SiLA 2, or equivalent), building real-time data pipelines for multimodal experimental outputs. - Architect policy training pipelines combining teleoperation data, synthetic demonstrations, reinforcement learning, and imitation learning for dexterous lab manipulation. - Build production-grade agentic runtime systems — failure detection, retry logic, exception handling, and human-handoff protocols — for unattended experimental sessions. - Design and execute autonomous experimental campaigns applying active learning, Bayesian optimization, or RL to drive iterative materials discovery. - Drive technical design reviews and set scientific direction for the autonomous lab platform. A day in the life You build the Physical AI systems that power robotics in autonomous science lab, one where foundation models generate hypotheses, robots execute experiments, and closed-loop optimization discovers materials that did not exist yesterday. You train manipulation policies in simulation, transfer them to a physical cobot, and watch real chemistry validate (or invalidate) an AI-generated theory. The signal here is not a metric on a dashboard; it is a synthesizing and testing novel material with measurable sustainability impact. If you want your research to have physical weight, this is the lab. About the team Sustainability Science and Innovation (SSI) is a multi-disciplinary research team within WW Sustainability combining science, ML, economics, and engineering. The autonomous laboratory is a new capability being built from the ground up. You will work alongside computational materials scientists, chemists, and ML engineers — with access to AWS-scale compute and Amazon's supply chain for hardware. The work targets sustainability outcomes across packaging, building materials, and alternative fuels.

We are seeking Data Scientist II with strong science application skills to join our Device Economics team. This role will focus primarily on Amazon's innovative devices and services (e.g. Echo Family of Devices), working at the intersection of economic modeling, forecasting science, and business strategy. The ideal candidate will be responsible for pre-launch forecasts, annualized overall forecasts, identifying substitution patterns, and partnering closely with product managers and marketing managers to understand the evolution of the Devices portfolio. Key job responsibilities Forecasting & Modeling 1. Develop and maintain pre-launch forecasts and annualized overall forecasts for Amazon Devices 2. Identify and model substitution patterns across the device portfolio 3. Build economic and financial models to support demand planning and business decisions 4. Formulate relevant analytical frameworks to address key economic issues in device forecasting Science Communication & Collaboration 1. Explain complex science models and methodologies to non-technical stakeholders including product managers and marketing managers 2. Collaborate with economists, data scientists, and applied scientists across Decision Science 3. Present results of analyses to cross-functional teams and leadership 4. Build trust in science models and forecast outputs with product teams Innovation & Strategic Thinking 1. Think creatively about ways that leading-edge analytics and emerging data sources can address Devices' most pressing business challenges 2. Help internal teams leverage analytic tools to better manage innovation 3. Conduct empirical studies and perform quantitative and qualitative research 4. Identify opportunities to improve forecasting accuracy and business impact Cross-Functional Partnership 1. Work closely with product managers and marketing managers to understand portfolio evolution and business strategy 2. Support DSO leadership in quarterly business reviews and strategic planning A day in the life Your days will be split between refining and building models and working with business leaders to interpret them. You own science-based forecasts that can directly impact Amazon's bottom line on the order of multi-million dollar decisions. - You will perform model refreshes or updates to analyses as needed; and, - You will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems. About the team The Decision Science team within DSO (Device Supply Organization) is responsible for forecasting and demand planning initiatives across Amazon Devices. The DSO team of 300+ engineers, scientists, and PMs applies quantitative methods and data-driven approaches to replace judgment-based decisions with science-driven forecasts. Decision Science focuses on lifetime demand forecasting using econometric and machine learning models for rapid reforecasting, mix adjustments, and portfolio management for new product launches. We also inform to go/no-go investment decision for new product initiatives

Have you ever wondered how Amazon predicts when your order will arrive and how we ensure that it actually arrives on at the promised date/time? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's logistics network including our planes, trucks, and vans sound exciting to you? If so, then we want to talk with you! At Amazon's Supply Chain Optimization Technologies (SCOT), we are tasked with optimizing the fulfilment on customer orders so that we fulfil all orders worldwide in the most intelligent manner while ensuring Amazon customers get their orders on time. Amazon Fulfillment Planning & Execution (FPX) Science team within SCOT- Fulfilment Optimization group is seeking a Senior Applied Scientist with expertise in Optimization and a proven record of solving business problems through scalable Optimization solutions. FPX Science tackles some of the most mathematically complex challenges in transportation planning and execution space to improve Amazon's operational efficiency worldwide. We own Amazon’s global fulfilment planning and execution. The team also owns the short and mid-term-term network planning and execution that determines the optimal flow of customer orders through Amazon fulfilment network. This includes developing sophisticated math models that assign orders to fulfilment centres to be picked and packed and then planning the optimal ship method in terms of cost, speed and carbon impact to deliver to the customer. These plans drive downstream decisions that are in the billions of dollars at Amazon Scale worldwide! The systems we build are entirely in-house, and are on the forefront of both academic and applied research in large scale supply chain planning, optimization, machine learning and statistics. These systems operate at various scales, from real-time decision system that completes thousands of transactions per seconds, to large scale distributed system that optimize Amazon’s fulfilment network. Your tech solution will have large impacts to the physical supply chain of Amazon, and play a key role in improving Amazon consumer business’s long-term profitability. If you are interested in diving into a multi-discipline, high impact space this is the team for you. We’re looking for a passionate, results-oriented, and inventive Senior Applied Scientist who can create and improve optimization models for our outbound transportation planning and execution systems. In addition, you will be working on design, development and evaluation of highly innovative Optimization models for solving complex business problems in the area of outbound transportation planning systems. Watch http://bit.ly/amazon-scot to get the big picture. Key job responsibilities As a Senior Applied Scientist, you will propose and deploy solutions that will likely draw from a range of scientific areas such as Optimization, machine learning, advanced statistical modeling, and graph models. You will be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry, with some of the best product managers, research scientists, statisticians, and software engineers to integrate scientific work into production systems. You will bring deep technical expertise in the area of Mathematical Optimization, and play an integral part in building Amazon's Fulfillment Optimization systems. Other responsibilities include: * Design, development and evaluation of highly innovative Math models for solving complex business problems. * Research and apply the latest Optimization techniques and best practices from both academia and industry. * Think about customers and how to improve the customer delivery experience. * Use and analytical techniques to create scalable solutions for business problems. * Work closely with software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. * Technically lead and mentor other scientists in team. * Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. A day in the life This is a great role for someone who likes to learn new things. You will have the opportunity to learn all about how Amazon plans for and executes within its logistics network including Fulfillment Centres, Air Stations, Sort Centres, Delivery Stations, and more. You will need to meet with many internal customers to understand their business and their challenges as you develop an effective solution to enable us to collectively scale. Our leadership is very interested in this effort so you will find lots of opportunities to get your ideas and plans in front of them for evaluation and alignment. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready mathematical optimization solutions. A successful candidate is able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Fulfillment Planning & Execution Science team contains a group of scientists with different technical backgrounds including Operations Research and Machine Learning who will collaborate closely with you on your projects. Our team directly supports critical functional areas across Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We tackle some of the most mathematically complex challenges in facility and transportation planning and execution to improve Amazon's operational efficiency worldwide and at a scale that is unique to Amazon. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges. We disambiguate complex supply chain problems and create solutions to solve those problems at scale.