Careers

Are You Ready to Redefine How the World Receives Its Packages? What if your algorithms defined the most efficient path for millions of deliveries — every single day? At Amazon, we're building the science that makes that possible, and we're looking for exceptional scientists to help lead the way. The Last Mile Routing & Planning organization develops the software, algorithms, and tools that power the "magic" of home delivery. Our planning and routing intelligence systems drive billions of daily decisions — enabling safe, efficient, and frustration-free routes for drivers across the globe. What You'll Do In this role, you'll sit at the intersection of state-of-the-art research and real-world impact. You will: - Design and build algorithms that solve large-scale, complex logistics problems - Synthesize data from diverse sources to identify high-value business opportunities - Provide research direction and data-driven insights to guide strategic decisions - Translate complex technical approaches into clear communication for scientists, engineers, and business stakeholders - Partner closely with scientists and engineers in a collaborative, high-impact environment What You'll Work On We have an exciting and growing portfolio of research areas, including: - Routing for same-day and grocery deliveries - Planning for electric and autonomous vehicles - District-level and stop-level planning - Forecasting solutions for diverse delivery programs All of this is powered by the latest methods in Operations Research (OR), Machine Learning (ML), and Generative AI — at a truly global scale. Successful candidates will lead one or more of these problem spaces. What We're Looking For - Deep expertise in Operations Research and/or Machine Learning methods - Proven experience applying these methods to large-scale, real-world business problems - Ability to translate models into production-ready code in Python or Java - Strong communication skills — you can explain complex technical concepts to diverse audiences - A bias for action and an iterative mindset when tackling ambitious research challenges Why Amazon We're passionate about your growth. Whether you want to explore emerging technologies, take on broader scope, or accelerate your career trajectory, we'll invest in helping you get there. Our business is scaling fast — and so are the opportunities for the people who build it. If you're driven by the challenge of optimizing one of the world's most complex logistics systems and excited to see your work impact millions of customers daily, we'd love to hear from you. Key job responsibilities - Invent and design novel solutions for scientifically complex problem areas, and identify opportunities for invention within existing and new business initiatives - Deliver large-scale, high-impact solutions to complex problems in support of medium-to-large business goals - Shape the design of scientifically complex software systems, personally contributing significant portions of the critical scientific novelty - Apply mathematical optimization, machine learning, and Generative AI techniques to develop solution methodologies for in-house decision support tools and software - Research, prototype, simulate, and experiment with models — and actively participate in their production-level deployment in Python or Java - Engage with the broader scientific community by publishing research articles and participating in leading research conferences

We are looking for a Senior Inference Engineer to own inference for real-time multimodal conversational AI. This is a full-stack inference role: you will work across the entire path a model takes from research to production — shaping model architecture so it is servable, building the real-time runtime that serves it within hard latency budgets, and building the offline systems that train and reinforce it. You will operate at the boundary of Science and Inference, taking frontier-scale speech and audio models and making them run within real-time latency budgets on production hardware. You will co-design architectures with scientists to make them inference-friendly from inception, own the low-latency streaming serving path, and build the training and reinforcement-learning infrastructure that closes the loop. You will have the compute, data, and runway to solve problems that few teams in the world are positioned to tackle. As a Senior Engineer, you will own a significant area of the inference stack end to end, drive its technical execution, contribute to the team's roadmap, and work closely with scientists and hardware partners to ensure our models run fast enough to feel human in real time — and at a cost that makes them viable at scale. You may go deep in one of the areas below while contributing across the others. Key job responsibilities Model Architecture & Inference Co-Design • Partner with research scientists to make model architectures servable from inception — surfacing the latency, memory, and cost implications of architecture choices before they are locked in • Implement and optimize the inference path for large-scale multimodal models — attention and KV-cache mechanisms, multimodal/autoregressive decoding, and the compute primitives on the critical path Apply efficiency techniques across the stack — quantization (per-tensor/per-channel/per- group, INT8/FP8/BF16), speculative decoding, operator fusion, and paged KV-cache — and quantify their quality/latency trade-offs • Develop and tune high-performance kernels for critical operations where off-the-shelf implementations leave performance on the table, integrating them into production serving with minimal overhead • Profile end-to-end performance with tools such as Nsight Compute/Systems and roofline analysis to identify and eliminate bottlenecks in large-scale inference workloads Real-Time & Interactive Runtime • Own the real-time serving path for streaming multimodal conversational AI, meeting sub- second, streaming latency budgets under concurrent session load • Build and tune continuous batching, scheduling, and preemption to balance throughput against per-request latency SLAs for interactive workloads • Customize production serving frameworks (e.g., vLLM, PyTorch) for real-time streaming generative models that fall outside standard LLM serving patterns — sustained low-latency output under concurrent session load • Implement multi-GPU inference (tensor parallelism, collective communication) for latency- critical paths, and drive cost toward parity with existing production baselines • Establish latency, throughput, and cost benchmarking, and publish the operational metrics that gate deployment Offline Systems: Training, RL & Evaluation Infrastructure • Build and scale the offline inference systems behind post-training — high-throughput rollout generation and reward-model serving for reinforcement learning (RL/RLHF/RLAIF) • Ensure train/serve consistency — that the inference path used in RL and evaluation faithfully matches production online behavior (e.g., parity across sampling and logit processing) • Work with the evaluation team to enable offline inference that captures the quality dimensions unique to real-time conversation — latency sensitivity, audio quality, and interaction naturalness

The Amazon GDS-MOP (modeling, Optimization and Planning) Science team is seeking an exceptional Applied Scientist with strong operations research and optimization expertise to develop production solutions for one of the most complex systems in the world: Amazon's Fulfillment Network labor capacity planning. At MOP Science, we design, build, and deploy optimization, statistics, machine learning, and GenAI/LLM solutions that power Amazon Labor Planning systems (ALPS) running across Amazon Fulfillment Centers worldwide. We solve a wide range of challenges encountered throughout the network, including labor planning and staffing, pick scheduling, stow guidance, and capacity risk management. We are tasked with developing innovative, scalable, and reliable science-driven production solutions that exceed the published state of the art, enabling systems to run frequently (ranging from every few minutes to every few hours per use case) and continuously across our large-scale network. Key job responsibilities As an Applied Scientist, you will collaborate with other scientists, software engineers, product managers, and operations leaders to develop optimization-driven solutions using a variety of tools and observe direct impact on process efficiency and associate experience in the fulfillment network. Key responsibilities include: • Develop understanding and domain knowledge of operational processes, system architecture and functions, and business requirements • Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approaches • Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges • Create prototypes and simulations for agile experimentation of devised solutions • Advocate for technical solutions with business stakeholders, engineering teams, and senior leadership • Partner with engineers to integrate prototypes into production systems • Design experiments to test new or incremental solutions launched in production and build metrics to track performance About the team 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

Have you ever placed an order on Amazon and wondered how it got to you so fast? Behind that speed is a massive transportation network generating billions of data points daily. We need someone who can turn that data into clarity. Come join the Network Engineering, Scheduling and Technology (NEST) Science team within Amazon Transportation Services. We are looking for a Data Scientist who is equal parts data engineer, visualization architect, and analytical modeler. You will own the end-to-end build process for data-driven solutions: identifying business needs, developing simulation and optimization models, building computationally efficient analytical tools, and narrating results through compelling data storytelling. This is not a dashboard-building role. You will work at the intersection of large-scale data processing, advanced analytics (including simulation and optimization), and data visualization, building tools that allow stakeholders to explore millions of records interactively, uncover patterns in network performance, and make data-driven decisions with confidence. The ideal candidate is a data wizard who thrives on wrangling massive datasets, building predictive and prescriptive models, architecting performant query and aggregation pipelines, and crafting visualizations that communicate complex findings with precision and clarity. You will own the full lifecycle, from problem identification and data extraction through modeling and simulation to production-grade analytical applications that narrate results back to stakeholders. You will collaborate closely with scientists, engineers, and product managers Key job responsibilities - Own the end-to-end analytical lifecycle: identify stakeholder needs, frame problems, build models, and narrate results through data tools and visualizations - Design and build production-grade analytical tools, BI applications, and interactive data products that enable self-service exploration of very large transportation datasets (billions of records) - Develop and enhance simulation and optimization models (discrete event simulation, agent-based modeling, mathematical optimization) applied to network planning and transportation operations - Architect computationally efficient data pipelines and aggregation strategies that support responsive, real-time or near-real-time visualization at scale - Develop advanced data storytelling artifacts that communicate complex network dynamics, trends, and anomalies to technical and non-technical stakeholders - Build and maintain reusable visualization frameworks and libraries tailored to transportation network data (routing, scheduling, flow, capacity) - Work with large-scale data platforms (Redshift, Spark, S3, Athena) to extract, transform, and model data for analytical consumption - Develop code (Python, SQL, Scala) for data processing, statistical modeling, simulation, and building automated analytical workflows - Collaborate with Applied Scientists, Research Scientists, Software Engineers, and Product Managers to integrate analytical tools into broader planning and decision-support systems - Define and implement best practices for data visualization performance, including sampling strategies, level-of-detail rendering, and progressive loading for large datasets - Communicate findings, methodology, and recommendations through compelling written and verbal presentations to leadership and business customers About the team The Network Engineering, Scheduling, and Technology (NEST) Science Team prototype, build, and productionize mathematical models that reduce transportation cost and improve customer experience in Amazon's Middle Mile network. Equipped with techniques from Operations Research, Machine Learning and Simulation, these models are used to govern scheduling and equipment selection of hundreds of thousands of truck movements, optimize network configurations, determine the transit times between nodes, and simulate network flow under uncertainty for informed decision making. Our core team consists of Applied, Data, and Research Scientists along with technical Product Managers that come from diverse backgrounds.

Amazon serves hundreds of millions of customers. Each one has a unique history of purchases, preferences, and behaviors. Our team's mission: turn that history into real-time contextual intelligence that makes every Amazon experience feel personal. We're hiring an Applied Scientist to push the boundaries of what's possible with LLMs, semantic retrieval, and customer understanding at scale. The problem space: Imagine a system that can instantly synthesize years of customer signals — what they bought, what they love, what they're planning — and surface the exact right context for any experience, in milliseconds. That's what we build. It's equal parts information retrieval, generative AI, and systems engineering. Why this role: 1. Scale: Your models will serve 1,500+ requests per second across Amazon's largest surfaces. 2. Impact: Direct revenue attribution in the hundreds of millions — your work shows up in customer experiences the same week. 3. Frontier tech: Fine-tuning LLMs, building custom embedding models, designing retrieval architectures that balance quality with sub-100ms latency constraints. 4. Data richness: Access to one of the most comprehensive customer behavior datasets anywhere. 5. Ownership: End-to-end — from research to production deployment to metric evaluation. Key job responsibilities 1. Invent new approaches to contextual retrieval, relevance scoring, and LLM-based summarization. 2. Fine-tune and evaluate language models for domain-specific understanding. 3. Design experiments that measure real customer impact, not just benchmark scores. 4. Ship production systems and iterate based on live metrics. 5. Collaborate across teams — Alexa, Search, Recommendations — as a platform that powers them all. 6. Mentor team members and shape the technical direction of our roadmap. Please visit https://www.amazon.science for more information. A day in the life You'll analyze large-scale behavioral data, design experiments, and build models that ship to production. You'll work closely with engineers to ensure your science translates into low-latency, high-reliability systems. You'll present findings to leadership and influence product strategy. Some weeks you'll be deep in model architecture; other weeks you'll be debugging a relevance gap in production. Every day, your work reaches real customers. About the team We're a small, high-impact team that values scientific rigor and engineering craft equally. The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.

Join Amazon's AOP (Analytics Operations and Programs) RoW ((Rest of World) Research Science Team to revolutionize supply chain planning across multiple geographies including India, Japan, Mexico, Brazil, MENA, Australia, and Singapore. As an Operations Research Scientist, you'll develop optimization solutions using simulations, integer programming, and heuristic search to solve complex short-term capacity planning challenges that directly impact millions of customers worldwide. Key job responsibilities We are looking for an Operations Research Scientist to develop & support our Short term capacity planning initiatives using Simulations, Integer Programming, Linear Programming or Heuristic search. This Scientist, will work closely with our program partners to define business requirements, build data pipeline, write optimization code, deep dive on solution quality and drive adoption with operations. The employee will also be responsible for interfacing with global science teams to help launch their tools to new geographies. A day in the life The employee will work with our program partners to find new opportunities for building/launching decision support tools for our Supply Chain planning teams. This will include 1) Understanding the current planning process for program teams and tools available to them. 2) Determining the gaps in the tools/decision making through data analysis or simulation systems. 3) Determining the best possible tool to solve for the current gaps. 4) Launch or develop the identified tool through coding or solution deep dives and scenario creation. About the team AOP (Analytics Operations and Programs) team supports supply chain processing for the multiple geographies like IN, Japan, Mexico, Brazil, MENA, AU & SG. The research team works to support network design, labor planning and capacity planning processes through launching decision support tools for planning or execution.

** Join Our Innovative Computer Vision Team at Amazon, Australia ** Are you passionate about developing computer vision models to transform the shopping experience for fresh produce and build AI models for fresh monitoring at scale? We invite you to be part of our high-performing Computer Vision team at Amazon, Australia. As a member of our international Machine Learning group, you will play a key role in building AI solutions that leverage vast amounts of Amazon data and cloud computing resources. Our mission is to build next-generation AI systems that monitor fresh produce 24/7 and ensure we deliver the best quality produce to our customers. We are seeking talented Computer Vision Scientists with a Ph.D. in a related field. This is an opportunity for you to build innovative AI techniques that tackle real-world business challenges. Join a team dedicated to advancing AI technology at Amazon and transforming it into impactful business solutions. #austechjobs Key job responsibilities - Develop scalable machine learning and computer vision solutions for the fresh monitoring system - Analyze and extract meaningful insights from large volumes of Amazon’s data to automate and enhance content - Design, build, and evaluate generative AI models tailored to our business use cases - Communicate clearly with business stakeholders to understand and align on requirements - Conduct s.o.t.a. research and implement novel machine learning techniques to solve customer problems - Mentor interns and junior scientists

AWS Elastic Compute Cloud (EC2) Capacity Research and Engineering Org is looking for an experienced applied optimization expert. This leader will join the Onhand Packing Science team to design, implement, and scale decision-making algorithms to optimize EC2’s use of existing capacity. The team owns the optimization of all aspects of virtual instances placement across EC2, from high level supply shaping right down the final physical machine a customer’s instance gets allocated to. Given the sheer scale of EC2 any improvement efficiency within capacity is utilized has significant business impact. Key job responsibilities We are seeking an expert with a strong background in mathematical optimization with excellent modeling skills, with experience using both exact and heuristic methods. The domain requires a tight collaboration with engineering teams, to ensure we are able to quickly move from modelling and prototyping to full scale production usage. You should be confirmable with the needed statistical approaches for production A/B tests as well as have a solid understanding experimental design more broadly. Being successful in the role requires having the scientific breadth to understand the interactions between different phases of a project from data analysis through to production, including resolving issues after rollout. You will be hands-on with the mathematical modeling and implementation, and will also contribute to the design of the engineering system with the scalability, extensibility, maintainability, and correctness of the optimization engine in mind. You will review approaches by other scientists and engineers in terms of business relevance, technical validity, engineering / science interface, and computational performance. You will mentor and lead junior scientists by example. Communicating your results to guide the direction of the business and working with software development teams to implement your ideas in code is key to success. You will write technical, and less frequently, business documents that influence engineering investments and business direction. Collaborating with other scientists, software engineers, and product managers, you will develop creative, novel, and data-driven approaches to improve our existing cloud compute offerings and define new ones in a fast-paced and quickly changing environment, improving the experience of our customers and impacting the bottom line of EC2. About the team A day in the life 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. 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. Inclusive Team Culture Here at AWS, 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. 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.

The Ads Marketing Decision Science team builds intelligent, data-driven systems that transform advertiser experiences through precise personalization and automated optimization. We decode complex patterns in advertiser behavior, content effectiveness, and performance signals to power real-time, contextual marketing decisions at scale — moving Amazon Ads from rules-based relevancy to true AI-driven personalization. Our work spans four pillars: Advertiser DNA (behavioral fingerprinting to predict advertiser needs and growth opportunities), Content Intelligence (frameworks to evaluate, select, and generate marketing content aligned to advertiser context), Automated Decision Systems (ML-powered audience targeting and next-best-action recommendations), and Gen-AI Applications (contextual, natural interactions across marketing touchpoints). As a Senior Applied Scientist on the team, you will be at the forefront of our Gen-AI applications, leading the science behind conversational and agentic experiences that help advertisers grow. This role demands a strong foundation in machine learning and in LLM/NLP — deep fundamentals that you apply to build robust, production-grade systems rather than treating models as black boxes. In particular, you will own the development of our chatbot capability — designing the agentic reasoning, retrieval, and evaluation systems that make these interactions accurate, helpful, and trustworthy. You will set the technical vision, innovate on behalf of our customers, and take solutions end-to-end from inception to production. You will partner closely with engineering to deploy at scale and low latency, and with product and business teams to ensure the experience meets real advertiser needs. Key job responsibilities • Lead the design and development of the chatbot/agentic AI capability for WeChat and other third-party channels, from concept through production. • Bring strong ML and LLM/NLP fundamentals to bear on system design — grounding architecture and modeling choices in a deep understanding of the underlying methods. • Architect and build agentic AI systems — planning, tool use, and multi-step reasoning — grounded in Retrieval-Augmented Generation (RAG) over Amazon Ads knowledge sources. • Apply reinforcement learning and model fine-tuning (e.g., instruction tuning, RLHF/RLAIF, preference optimization) to adapt large language models to our domain and channels. • Define and operationalize rigorous LLM evaluation: golden sets, faithfulness/groundedness, precision/recall, and human-in-the-loop evaluation mechanisms that reliably measure and improve quality. • Own applied engineering quality of the science stack — PyTorch modeling, well-designed APIs, and latency/cost optimization for real-time, production-grade interactions. • Collaborate with engineering, product management, and business teams to define requirements and ship measurable customer impact. • Drive continuous improvement through experimentation, iterative development, testing, and optimization. • Translate complex scientific challenges into clear, impactful solutions for business stakeholders. • Mentor and guide junior scientists, fostering a collaborative, high-performing team culture, and engage the broader scientific community through presentations, publications, and patents. About the team We are a team of Applied Scientists, Research Scientists, Data Scientists, and Business Intelligence Engineers with deep expertise in ML, NLP, Gen-AI, RL, and causal inference, from a diverse range of backgrounds. We partner closely with strong engineers, product managers, and sales leaders who bring ads-industry depth and experience building scalable modeling and software solutions.

Amazon Stores Finance Science (ASFS) is committed to integrating industry leading scientific methodologies into our financial processes. Our mission is to collaborate with finance and business partners to leverage advanced scientific and economic products to optimize insights, analysis, and decision-making. Our primary focus lies in developing solutions for Finance's enduring challenges and opportunities, encompassing financial controllership, planning, and operational efficiency. We are seeking a Applied Scientist to propel Gen AI acceleration within Finance. This role will focus on leveraging large language models and agentic Ai to enhance decision-making processes, automate complex controllership processes, and improve operational efficiency in Stores Finance. Key job responsibilities - Develop approaches to automate and enhance financial processes using state-of-the-art language models - Partner with business stakeholders to identify high-impact opportunities for AI-driven transformation - Work with Software Engineering and Data Engineering to implement solutions that scale to the entire organization A day in the life Are you interested in solving complex problems at the intersection of Science/Economics and Finance? And doing this while working in the Finance organization supporting one of the world’s largest and most complex and dynamic businesses? This is a unique opportunity for qualified individuals to drive the entire life cycle of highly impactful, real-world ML applications, focused on use of advanced generative AI technology. If you thrive in a fast-paced, dynamic environment, and are passionate about driving impact, we encourage you to apply! About the team WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing science/econ products that empower controllership, improve business decisions and financial planning by understanding drivers, and innovating Gen AI capabilities for efficiency and scale.