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

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.

Amazon is looking for an Applied Scientist to help build next generation selection/assortment systems. On the Specialized Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection of the products that Amazon offers in our limited shelf assortment problems world wide. This includes products for our fastest delivery, perishable grocery offerings, and other emerging Amazon delivery programs. The selection is generated with a series of Machine Learning (ML) and optimization models to best cater to customer purchase intents under limited warehouse capacity. We build tools and systems that enable our partners and business owners to scale themselves by leveraging our problem domain expertise, focusing instead on introspecting our outputs and iteratively helping us improve our models rather than hand-managing their assortment. We partner closely with our business stakeholders as we work to develop state-of-the-art, scalable, automated selection management systems. As an Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including supervised and unsupervised machine learning, large language models, mixed integer linear programs (MILPs), reinforcement learning, causal inference, and experiment designs. Some critical research areas in our space include modeling substitutability between similar products, complementarity and basket building, measuring speed sensitivity of products through experiments, optimizing assortment under operational and capacity constraints, and supply and demand forecasting. Key job responsibilities You will be an end-to-end owner for the projects you support. Responsibilities include: Understanding business requirements and existing challenges and map them to the right scientific solution; Designing effective, scalable, and achievable solutions to key business problems; Developing the right set of metrics to evaluate efficacy of your models and solutions; Prototyping and analyzing new models and business logic; Productionizing your scientific solutions, including writing production-quality critical path code; Communicating, both written and verbally, with both technical and business audiences throughout each project; Publishing findings in internal and/or external conferences and interfacing with the scientific community; Mentoring and developing the scientist community across the organization

Are you passionate about solving complex problems and protecting one of the world’s largest cloud platforms? The AWS Payments and Fraud Prevention team is looking for an innovative Applied Scientist to help keep AWS a safe and trusted environment for millions of customers worldwide. In this role, you will design, build, and deploy machine learning models that detect, prevent, and mitigate fraudulent activity across the AWS ecosystem. You will work with massive, real-world datasets, develop new detection strategies, and apply advanced and practical technologies to tackle ever-evolving threats. You will also explore Generative AI (GenAI) techniques to uncover new fraud patterns and strengthen our fraud defenses. At AWS, we support hundreds of thousands of businesses, powering billions of transactions every day. Fraudsters are constantly innovating — and so are we. If you enjoy thinking like a fraudster, building resilient defenses, and making a real-world impact, we invite you to join us and help shape the future of secure cloud computing. Key job responsibilities * Design, build, and deploy machine learning models to detect, prevent, and mitigate fraudulent activities across the AWS platform. * Analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats. * Explore and apply GenAI techniques, including large language models (LLMs), synthetic data generation, and adversarial simulations to enhance fraud detection capabilities. * Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions. * Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics. * Continuously monitor model performance and improve robustness against adversarial behaviors and evolving fraud tactics. * Communicate findings and technical insights clearly and effectively to both technical and non-technical audiences. * Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization. About the team AWS Payments and Fraud Prevent is responsible for detecting & mitigating AWS account risks. You’ll be part of a team of Scientists, Analysts, and Technical & non-Technical Program Managers. The team’s goal is to identify and neutralize fraudsters from unauthorized access to legitimate AWS customers accounts. We have a formal mentor search application that lets you find a mentor that works best for you. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members.

Amazon Devices is an inventive research and development company that designs and engineers high-profile devices like Echo, Fire Tablets, Fire TV, and other consumer devices. We are looking for an exceptional data scientist to join our Edge AI Product Science team to advance the state-of-the-art in developing efficient multimodal deep learning models across our product portfolio. Through close hardware-software integration, we design and train models for resource efficiency across the hardware and software tech stack. Edge AI Product Science team is looking for a talented Data Scientist who will introduce and model best practices around our dataset curation, annotation pipelines, and data driven model development. This role will take a data-first approach to improving model and system performance and work alongside other scientists to develop new evaluation methods for multimodal language models and agents for our devices, including audio and vision experiences. Key job responsibilities - Collaborate with cross-functional engineers and scientists to advance the state of the art in multimodal model evaluations for devices, including audio, images, and videos - Invent and validate reliability for novel automated evaluation methods for perception tasks, such as fine-tuned LLM-as-judge - Develop and extend our evaluation frameworks to support expanding capabilities for multimodal language models - Analyse large offline and online datasets using Python or similar to understand model gaps, develop methods to interpret model failures, and collaborate with training teams to enhance model capabilities for product use cases - Work closely with other scientists, compiler engineers, data collection, and product teams to advance evaluation methods A day in the life As a Data Scientist with the Edge AI Product Science team, you'll contribute to innovative methods for evaluating new product experiences and discover ways to use data to enhance our model capabilities and enrich our customer experiences. You'll have opportunities to collaborate across teams of engineers and scientists to bring algorithms and models from inception through prototype all the way to production. About the team Our Edge AI Product Science team brings together our unique skills and experiences to deliver state-of-the-art multimodal AI models that enable new experiences on Amazon devices. We strive to deliver customer experiences driven by AI at the edge which are only possible with tight model, hardware, and system co-design.

The Amazon Robotics team is seeking an experienced Applied Scientist to join our team. In this role you will apply the latest trends in research to solve real-world problems in robotics and AI. You will collaborate with a team of scientists and engineers building these applications. We holistically design, build, and deliver end-to-end robotic systems. Our team is also responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, machine learning scientists, software engineers, and hardware engineers to collaborate and deploy systems in the field. Key job responsibilities • Research, design, develop, and evaluate complex perception, motion planning, and decision making algorithms integrating across multiple disciplines and leveraging machine learning. • Create experiments and prototype implementations of new learning algorithms and prediction techniques. • Work closely with software engineering team members to drive scalable, real-time implementations. • Collaborate with machine learning and robotic controls experts to implement and deploy algorithms, such as machine learning models. • Collaborate closely with hardware engineering team members on developing systems from prototyping to production level. • Represent Amazon in academia community through publications and scientific presentations. • Work with stakeholders across hardware, science, and operations teams to iterate on systems design and implementation. About the team Watch this video to learn more about Vulcan Pick team in Amazon Robotics: https://www.amazon.science/publications/vulcan-pick-a-robotic-system-for-picking-targeted-objects-from-fabric-pods

Amazon’s Middle Mile transportation network runs on physical assets and operations that generate an enormous volume of imagery and video. The Network Engineering, Scheduling, Technology (NEST) Science team within the Amazon Transportation Services organization is looking for an Applied Scientist with deep Computer Vision (CV) expertise and the versatility to apply machine learning broadly, to help turn that visual data into automated, operational decisions, spanning asset condition, automated inspection, object and component detection, and other visual understanding problems across the network. In this role, you will develop, train, and productionize computer vision models across a portfolio of high-impact use cases and business challenges, owning the scientific approach from problem formulation through production. You will work closely with other scientists, business owners, and engineering teams to advance modeling approaches, design rigorous experiments to validate them, and launch them to production. This is a hands-on applied science role with a broad scope and direct, measurable business impact. Key job responsibilities -Develop and apply visual perception and representation learning across image and video domains, including recognition, detection, segmentation, and tracking, with deep spatial and temporal reasoning enabled by modern deep learning and foundation-model architectures. -Own large-scale model training, fine-tuning, and learning from heterogeneous or weakly supervised data, including self-supervised and semi-supervised techniques. -Tackle real-world CV challenges at scale: large unlabeled datasets, class imbalance, high visual variability, and inconsistent capture conditions (angle, lighting, occlusion, motion blur). -Partner with product/program, operations, and engineering stakeholders to translate operational problems into well-defined CV objectives with measurable success criteria. -Design rigorous evaluation frameworks with explicit precision/recall tradeoffs and operating-point selection tied to real-world business cost, including the cost asymmetry between false negatives and false positives. -Build and maintain custom training and inference pipelines, and partner with engineering teams to deploy models into production. -Frame ambiguous operational problems from first principles and select the right approach for each, applying CV where it’s the best tool and other machine learning or simpler methods where they are not.

Employer: Twitch Interactive, Inc. Position: Applied Scientist III - AMZ27946.1 Location: San Francisco, CA Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $192200 - $260000) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000