Careers

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering AI-powered solutions that transform how advertisers make strategic decisions. We deliver billions of ad impressions and process massive volumes of advertiser data every single day. You'll work with us to pioneer breakthrough approaches in how AI agents access and reason over real-time advertiser data at scale. We are using generative AI and agentic systems to help advertising agents provide instant, strategic advice to millions of advertisers. You will need to invent new techniques for agent orchestration, context optimization, and code generation to ensure we're delivering accurate, trustworthy insights with minimal latency and token consumption. You'll create feedback loops to ensure our solutions are constantly evaluating themselves and improving. The Ads Real-Time Data Service team is seeking an exceptional Applied Scientist to research and develop novel approaches for agent-data interaction. The Ads Real-Time Data Service team is solving one of the most critical challenges in advertising AI: instant access to advertiser context. We're building the infrastructure that provides immediate, pre-computed access to advertiser data via Model Context Protocol (MCP) servers—an emerging standard for AI agent-data interaction. We're building summarized data for context using a mix of state of the art techniques like CodeAct and RAG-based embeddings, achieving a fundamental transformation in how AI agents interact with data. This role balances applied research (60%) with productionization (40%), giving you the opportunity to both advance the state of the art and see your innovations deployed at Amazon scale. Key job responsibilities Agent Orchestration & Optimization Research - Research and develop novel algorithms for agent-data interaction patterns that minimize latency, token consumption, and error rates - Investigate multi-agent orchestration strategies for complex advertiser queries requiring data from multiple sources - Develop techniques for automatic query optimization and caching strategies based on agent behavior patterns Large Language Model Context & Token Optimization - Invent new methods for compressing advertiser context representations while preserving semantic meaning and analytical utility - Research optimal metadata generation techniques that help large language models understand and reason over structured advertiser data - Design evaluations to measure the impact of different data representations on agent response quality and token efficiency - Develop adaptive context selection algorithms that dynamically choose relevant data based on query intent RAG-Based Embeddings & Semantic Search - Pioneer new RAG-based embedding approaches optimized for real-time advertiser data delivery with sub-second latency - Research and implement semantic search and retrieval techniques for advertiser datasets using vector embeddings - Design advertiser context frameworks that enable automatic schema mapping from advertiser concepts to data representations - Develop evaluation frameworks to measure performance across dimensions of latency, accuracy, and developer experience Experimentation & Productionization - Design and execute rigorous experiments comparing traditional API orchestration versus CodeAct patterns and RAG-based approaches across metrics like success rate, latency, token consumption, and response quality - Analyze large-scale advertiser interaction data to identify patterns, bottlenecks, and optimization opportunities - Collaborate with engineering teams to productionize research innovations and deploy them to 30+ advertising agents and skills - Establish evaluation metrics and benchmarks for agent-data interaction performance Cross-Functional Collaboration & Thought Leadership - Partner with agent builder teams to understand their data requirements and constraints - Work with platform engineers to implement and optimize MCP servers, data pipelines, and sandbox execution environments - Collaborate with product managers to translate research insights into product features and roadmap priorities - Stay current on latest advancements in agentic AI research, specifically in large language models, multi-agent systems, chain of thought reasoning, and autonomous agents Research Publication & Innovation - Author technical papers for top-tier conferences on agent orchestration, context optimization, RAG-based embeddings, and real-time data integration - File patents for novel techniques in agent-data interaction, token optimization, and CodeAct patterns - Present research findings at internal tech talks and external conferences - Mentor engineers and junior scientists on machine learning techniques, experimental design, and research methodologies A day in the life You start your morning analyzing experiment results from overnight runs comparing three evaluations for different RAG-based embedding approaches. The data shows that one of the embedding pattern is returning a significant improvement in accuracy. You create a spec file with the findings and start drafting a technical paper to be shared with Amazon AI forum. Mid-morning, you're in a design session with the engineering team discussing how to optimize RAG-based embeddings for semantic search over advertiser data. You propose using a hybrid approach combining dense and sparse embeddings to represent campaign metadata, enabling agents to find relevant campaigns through natural language queries while maintaining sub-second latency. You sketch out the architecture and discuss trade-offs between embedding model size, search latency, and accuracy. After lunch, you dive into advertiser interaction logs from advertising agents and skills. You're looking for patterns in how advertisers ask questions about their campaigns. You discover that 60% of queries follow a similar structure: filter campaigns by criteria, aggregate metrics, and compare to benchmarks. This insight leads you to design a new pre-computation strategy using RAG-based embeddings that could reduce query latency by 40%. In the afternoon, you collaborate with an Applied Scientist from an advertising agent team. They're seeing inconsistent results when agents try to calculate complex metrics across multiple campaigns. You investigate and discover the issue is related to how the agent interprets the advertiser context. You propose enriching the RAG-based embeddings with richer metadata descriptions and run experiments showing this improves calculation accuracy from 85% to 98%. Late afternoon, you're prototyping a new approach for adaptive context selection using RAG-based embeddings with the spec file you generated earlier. Instead of providing agents with all available advertiser data, you want to dynamically select the most relevant datasets based on query intent using semantic similarity. You build a quick proof-of-concept and test it on historical queries. The results are promising: 30% reduction in tokens with no loss in response quality. About the team The Ads Real-Time Data Service team is a diverse group of passionate engineers and scientists dedicated to advancing agent-data interaction technology for advertising AI. We value creativity, collaboration, and a commitment to excellence. Our team thrives on tackling complex problems at the intersection of real-time data engineering, AI agent systems, and large language model optimization—turning innovative research ideas into production systems that serve millions of advertisers. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. We have a broad mandate to experiment and innovate, working on problems in agentic AI, context optimization, RAG-based embeddings, and real-time data delivery. We celebrate both research excellence (papers, patents) and engineering impact (production systems serving 30+ advertising agents and skills). We maintain a sustainable pace with flexible work arrangements and a strong focus on work-life balance.

The Mission Build AI safety systems that protect millions of Alexa customers every day. As conversational AI evolves, you'll solve challenging problems in Responsible AI by ensuring LLMs provide safe, trustworthy responses, building AI systems that understand nuanced human values across cultures, and maintaining customer trust at scale. What You'll Build You'll pioneer breakthrough solutions in Responsible AI at Amazon's scale. Imagine training models that set new safety standards, designing automated testing systems that hunt for vulnerabilities before they surface, and certifying the systems that power millions of daily conversations. You'll create intelligent evaluation systems that judge responses with human-level insight, build models that truly understand what makes interactions safe and delightful, and craft feedback mechanisms that help Alexa+ grasp the nuances of complex customer conversations. Here's where it gets even more exciting: you'll build AI agents that act as your team's safety net—automatically detecting and fixing production issues in real-time, often before anyone notices there was a problem. Your innovations won't just improve Alexa+; they'll fundamentally shape how it learns, evolves, and earns customer trust. As Alexa+ continues to delight customers, your work ensures it becomes more trustworthy, safer, and deeply aligned with customer needs and expectations. Your work directly protects customer trust at Amazon's scale. Every innovation you create—from novel safety mechanisms to sophisticated evaluation techniques—shapes how millions of people interact with AI confidently. You're not just building products; you're defining industry standards for responsible AI. This is frontier research with immediate real-world impact. You'll tackle problems that require innovative solutions: training models that remain truthful and grounded across diverse contexts, building reward models that capture the nuanced spectrum of human values across cultures and languages, and creating automated systems that continuously discover and address potential issues before customers encounter them. You'll collaborate with world-class scientists, product managers, and engineers to transform state-of-the-art ideas into production systems serving millions. What We're Looking For * Deep expertise in state-of-the-art NLP and Large Language Models * Track record of building scalable ML systems * Passion for impactful research—where frontier science meets real-world responsibility at scale * Excitement about solving problems that will shape the future of AI Ready to work on AI safety challenges that define the industry? Join us. Key job responsibilities This is where you'll make your mark. You'll architect breakthrough Responsible AI solutions that become industry benchmarks, pioneering algorithms that eliminate false information, designing frameworks that hunt down vulnerabilities before bad actors find them, and developing models that understand human values across every culture we serve. Working with world-class engineers and scientists, you'll push the boundaries of model training—transforming bold research into production systems that protect millions of customers daily while withstanding attacks and delivering exceptional experiences. But here's what makes this role truly special: you'll shape the future. You'll lead certification processes, advance optimization techniques, build evaluation systems that reason like humans, and mentor the next generation of AI safety experts. Every innovation you drive will set new standards for trustworthy AI at the world's largest scale. A day in the life As a Responsible AI Scientist, you're at the frontier of AI safety—experimenting with breakthrough techniques that push the boundaries of what's possible. You partner with engineering to transform research into production-ready solutions, tackling complex optimization challenges. You brainstorm with Product teams, translating ambitious visions into concrete objectives that drive real impact. Your expertise shapes critical deployment decisions as you review impactful work and guide go/no-go calls. You mentor the next generation of AI safety leaders, watching ideas spark and capabilities grow. This is where science meets impact—building AI that's not just intelligent, but trustworthy and aligned with human values. About the team Our team pioneers Responsible AI for conversational assistants. We ensure Alexa delivers safe, trustworthy experiences across all devices, modalities, and languages worldwide. We work on frontier AI safety challenges—and we're looking for scientists who want to help shape the future of trustworthy AI.

The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire an Instrument Control Engineer to join our growing software team. You will work closely with our experimental physics and control hardware development teams to enable their work characterizing, calibrating, and operating novel quantum devices. The ideal candidate should be able to translate high-level science requirements into software implementations (e.g. Python APIs/frameworks, compiler passes, embedded SW, instrument drivers) that are performant, scalable, and intuitive. This requires someone who (1) has a strong desire to work within a team of scientists and engineers, and (2) demonstrates ownership in initiating and driving projects to completion. This role has a particular emphasis on working directly with our control hardware designers and vendors to develop instrument software for test and measurement. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon 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. Key job responsibilities - Work with control hardware developers, as a “subject matter expert” on the software interfaces around our control hardware - Collaborate with external control hardware vendors to understand and refine integration strategies - Implement instrument drivers and control logic in Python and/or a low-level languages, including C++ or Rust - Contribute to our compiler backend to enable the efficient execution of OpenQASM-based experiments on our next-generation control hardware - Benchmark system performance and help define key performance metrics - Ensure new features are successfully integrated into our Python-based experimental software stack - Partner with scientists to actively contribute to the codebase through mentorship and documentation We are looking for candidates with strong engineering principles, a bias for action, superior problem-solving, and excellent communication skills. Working effectively within a team environment is essential. As an Instrument Control Engineer embedded in a broader science organization, you will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life Your time will be spent on projects that extend functional capabilities or performance of our internal research software stack. This requires working backwards from the needs of science staff in the context of our larger experimental roadmap. You will translate science and software requirements into design proposals balancing implementation complexity against time-to-delivery. Once a design proposal has been reviewed and accepted, you’ll drive implementation and coordinate with internal stakeholders to ensure a smooth roll out. Because many high-level experimental goals have cross-cutting requirements, you’ll often work closely with other engineers or scientists or on the team. About the team You will be joining the Software group within the Amazon Center of Quantum Computing. Our team is comprised of scientists and software engineers who are building scalable software that enables quantum computing technologies.

Amazon launched the AGI Lab to develop foundational capabilities for useful AI agents. We built Nova Act - a new AI model trained to perform actions within a web browser. The team builds AI/ML infrastructure that powers our production systems to run performantly at high scale. We’re also enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up! Key job responsibilities This role will lead a team of SDEs building AI agents infrastructure from launch to scale. The role requires the ability to span across ML/AI system architecture and infrastructure. You will work closely with application developers and scientists to have a impact on the Agentic AI industry. We're looking for a Software Development Manager who is energized by building high performance systems, making an impact and thrives in fast-paced, collaborative environments. About the team Check out the Nova Act tools our team built on on nova.amazon.com/act

Are you interested in building the measurement foundation that proves whether targeted, cohort-based marketing actually changes customer behavior at Amazon scale? We are seeking an Applied Scientist to own measurement and experimentation for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing Analytics and Science) team. In this role, you will design and execute rigorous experiments that measure the effectiveness of audience-based marketing campaigns across multiple channels, providing the evidence that guides marketing strategy and investment decisions. This is a high-impact role where you will build measurement frameworks from scratch, design experiments that isolate causal effects, and establish the experimental standards for lifecycle marketing across EU. You will work closely with business leaders and the senior science lead to answer critical questions: does targeting specific cohorts (Bargain hunters, Young adults) improve efficiency vs. broad campaigns? Which creative strategies drive behavior change? How should we optimize marketing spend across channels? Key job responsibilities Measurement & Experimentation Ownership: 1. Own measurement end-to-end for lifecycle marketing campaigns – design experiments (RCTs, geo-tests, audience holdouts) that measure campaign effectiveness across marketing channels 2. Build measurement frameworks and experimental best practices that work across different activation platforms and can scale to multiple campaigns 3. Establish experimental standards and tooling for lifecycle marketing, ensuring statistical rigor while balancing business constraints Causal Inference & Analysis: 1. Apply causal inference methods to measure incremental impact of marketing campaigns vs. counterfactual 2. Navigate measurement challenges across different platforms (Meta attribution, LiveRamp, clean rooms, onsite tracking) 3. Analyze experiment results and provide optimization recommendations based on statistical evidence 4. Establish guardrails and success criteria for campaign evaluation About the team The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.

Amazon is seeking passionate, talented and inventive Applied Scientists with a strong machine learning background to help build Agentic AI solutions for Selling Partners and Retail users. Our mission is to elevate their experience through intelligent, collaborative Agentic AI solution powered by specialized agents that deliver seamless, personalized support at scale. Key job responsibilities As part of our AERO team in Amazon PGX, you will work alongside internationally recognized experts to develop novel solutions and modeling techniques to advance the state-of-the-art in Generative and Agentic AI. Your work will directly impact millions of our users in the form of products and services that improves their experience. You will gain hands on experience with Amazon’s native/partnered Large Language Models (LLMs), technologies like AgentCore, AgentMemory, Strands and Model Context Protocol (MCPs). We are also looking for talents with experiences/expertise in building large-scale and high-performing systems.

Are you driven by the challenge of solving complex problems that directly impact the safety and well-being of millions of Amazon Associates worldwide? Do you want to push the boundaries of AI to build innovative solutions that make workplaces safer and more efficient? If so, we invite you to join our WHS DataTech team as an Applied Scientist and take your career to the next level! At WHS DataTech, we leverage Large Language Models (LLMs), Computer Vision, and AI-driven innovations to develop industry-leading solutions that proactively enhance workplace safety. Our work spans real-time risk assessment, predictive analytics, and AI-powered insights, all aimed at creating a safer work environment at scale. As an Applied Scientist specializing in LLMs and Computer Vision, you will play a pivotal role in shaping our next-generation safety solutions. You’ll be at the forefront of innovation, designing and implementing AI-powered features that redefine workplace safety. Your work will drive strategic decisions, optimize system architecture, and influence best practices, ensuring our technology remains industry-leading. Key job responsibilities - Apply LLM model to analyze complex unstructured datasets and extract meaningful insights. - Collaborate with software engineers to implement and deploy machine learning (LLM or CV) solutions. - Conduct experiments and evaluate model performance, iterating and improving as needed. - Stay up-to-date with the latest advancements in machine learning and related fields. - Collaborate with cross-functional teams to understand business needs and identify areas for application of machine learning. - Present findings and recommendations to stakeholders and contribute to the overall research and development strategy. 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 WHS DataTech is a multidisciplinary team of scientists and engineers dedicated to building AI-powered solutions that improve workplace safety across Amazon. We work at the intersection of large-scale data, advanced machine learning, and computer vision, delivering innovations that enhance decision-making, streamline operations, and protect millions of associates worldwide. Our collaborative culture emphasizes scientific rigor, engineering excellence, and a strong mission focus on creating safer, more efficient workplaces.

As an Applied Scientist in Amazon Fullfilment Technology, you will lead the development of agentic systems to assist with operational decision making and orchestration. You will work building full agentic systems leveraging multi-agent orchestration, tool use, memory, and action execution. You will train LLMs using a combination of rejection sampling approaches, SFT, continual post-training, and Reinforcement Learning (RL). These systems are deployed to Amazon buildings, and you will also work on rigorous offline and online evaluations. Your work will leverage the latest LLMs to develop capabilities for agentic reasoning, coding and analytics. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication. Key job responsibilities - Generating training and preference data for specific use cases (reasoning trajectories, tool traces) - Reward modeling and policy optimization for LLMs: DPO, IPO, RLHF/RLAIF with PPO/GRPO, rejection sampling. - Supervised fine-tuning on step-by-step trajectories and tool-use traces - Verbal Reinforcement Learning and Continual Learning - RL for LLMs, Offline RL and off-policy evaluation - Agentic memory/state management; episodic and semantic memory; vector search; grounding with RAG. - Evaluation: developing decision quality metrics, scaling LLM-based evaluations. About the team Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and data science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. Learn more about AFT: https://tinyurl.com/AFTOverview

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities - Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.

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. Fulfillment Planning & Execution (FPX) Science team within SCOT- Fulfilment Optimization owns and operates OR/ML and simulation systems that continually optimize the distribution of tens of millions of products across Amazon’s warehouses in the most cost-effective manner, utilizing large scale optimization techniques and distributed computing in trying to reduce overall transportation costs while improving the customer experience. We are focused on saving hundreds of millions of dollars using Optimization, machine learning, and scalable distributed software on the cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing and supply. We’re looking for a passionate, results-oriented, and inventive scientist who can create and improve optimization models for our outbound transportation planning 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 and execution systems. You will work closely with our product managers and software engineers to disambiguate complex supply chain problems and create optimization solutions to solve those problems at scale. You will directly impact our direct customers, and even play with big data and incredible scale in the background. Watch http://bit.ly/amazon-scot to get the big picture. Key job responsibilities - Design, development and evaluation of highly innovative optimization 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. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and optimization 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 it's logistics network including Fulfillment Centers, Sort Centers, Delivery Stations, and more. In this role, you will be a design and develop Optimization models with significant scope, impact, and high visibility. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. From day one, you will be working with bar raising scientists, engineers, and designers. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work. 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. 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 Fulfillment Planning & Execution Science team contains a group of scientists with different technical backgrounds including Machine Learning and Operations Research, 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 ML and optimization solutions to solve those problems at scale.