Careers

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's Worldwide Grocery Stores (WWGS), Data & Science team is seeking an Applied Scientist to join our Sales & Operations Planning (S&OP) and Supply Chain Science team. In this role, you will help build machine learning models that improve how the Amazon Grocery Network plans and stocks its stores, where gaps between plan and reality lead directly to out-of-stocks, wasted product, higher costs, and degraded customer experience. You will contribute to the development and deployment of models across a range of grocery supply chain problems, including demand forecasting, customer preference modeling, and improving product availability, using time series, Bayesian and structural methods, and machine learning. You will work alongside senior scientists who will help you scope problems, review your designs and code, and grow your depth in supply chain science and production ML — and you will work closely with engineering partners, product owners, and business stakeholders to deliver measurable impact. Our models inform planning and inventory decisions across the grocery supply chain, many of them carried out by partner teams and the systems they own, so understanding how model errors land on stores, planners, and customers matters as much as improving offline metrics. You will participate in design and roadmap discussions, communicate clearly with technical and non-technical partners, and develop judgment about the trade-offs in the systems you contribute to. We are investing in Generative AI to advance supply chain workflows, moving from human-in-the-loop to AI-in-the-loop decision support. Opportunities include automating routine planner interventions, surfacing recurring sources of operational defects, and augmenting planner and scientist judgment with agentic tools. Key job responsibilities - Develop, evaluate, and deploy components of machine learning and statistical models for grocery supply chain problems, including demand forecasting, customer preference modeling, and product availability, with input and guidance from senior scientists. - Build models and mechanisms that reduce out-of-stocks and shrink, including identifying and helping correct upstream data and process issues that degrade them. - Translate business problems into well-defined scientific solutions with clear objectives, constraints, and success metrics, partnering with senior scientists on the more ambiguous ones. - Analyze model performance and downstream impact on inventory, availability, and capacity decisions; contribute to metrics that reflect business outcomes, not only offline model accuracy. - Prototype and evaluate Generative AI approaches in our supply chain workflows, including automated interventions, and help productionize the ones that prove out. - Partner with engineering teams to productionize models, contribute to data pipelines, and build scalable, maintainable science systems. - Monitor deployed models, investigate performance issues, and continuously improve model quality and calibration. - Communicate technical concepts and recommendations clearly through documentation, presentations, and design reviews with scientists, engineers, product managers, and business leaders. - Contribute to the internal scientific community through knowledge sharing and, where appropriate, research publications.

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

Amazon is looking for a passionate, talented, and inventive Data Scientist with machine learning background to help build industry-leading Speech and Language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML). Key job responsibilities Key job responsibilities Amazon is looking for a passionate, talented, and inventive Data Scientist with machine learning background to help build industry-leading Speech and Language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop data experiments, novel algorithms and data techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. You will gain hands on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding.

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

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

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.