Careers

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide

AWS Networking operates one of the largest and most complex networks on the planet. The team you'd join is responsible for the availability of that network — measuring how it performs for customers, predicting where it is most likely to degrade, and reshaping how we operate it as the workload grows. We are in the middle of a significant change in how network operations are run. Lessons from our recent work on automation, AI, and ML — including agentic systems that triage and mitigate incidents alongside engineers — are feeding into a broader rethink of where humans focus, where automation takes over, and how we measure whether either is working. We are looking for a Data Scientist to join the team in Sydney to drive the data science strategy behind that change. You will define the metrics that matter, own the evidence the team uses to make decisions, and measure whether each decision delivered the outcomes we expected. You'll be the data science voice on a team of senior network and software engineers — the person who decides what we measure, how we measure it, and what the numbers actually mean. Concretely, that means setting the analytical bar for the program, designing risk and reliability models against telemetry from millions of network devices, surfacing the patterns that drive customer-impact incidents, and turning that analysis into the dashboards and metrics our leaders use to set priorities. It also means owning the evaluations that tell us when a new piece of automation — including the agents we are rolling out to support engineers on the front line — is actually moving the needle on availability, and not just adding noise. If you are a scientist who wants to shape how a tier-one production network is run — using data to drive program strategy, not just to support it — at a scale no academic lab or startup can match, and you're at your best as the data science voice embedded in a team of engineers, this is the team for you. Key job responsibilities - Define and drive the data science strategy for the program — the metrics, the experiments, and what counts as evidence that a change worked - Lead the design and deployment of predictive risk and reliability models for network availability, using device failures, alarm telemetry, ticket data, and traffic signals - Own the evidence behind program decisions: where availability is at risk, where automation is ready to expand, where engineering effort has the highest leverage. Defend recommendations to senior technical and business audiences - Design and own the operational analytics and dashboards (Amazon QuickSight, Amazon CloudWatch, Python) used by senior leadership to track network health and the impact of operational change - Design and run experiments to evaluate the automation we are rolling out — including agentic systems supporting engineers on incidents — measuring whether each rollout improved availability - Drive data quality and classification improvements — event categorisation, root-cause attribution — so the program's metrics rest on solid ground - Build and own event-driven scoring pipelines (Python, SQL, AWS Lambda, Amazon S3, Amazon Athena) that keep the decide / measure / improve loop running - Bring statistical rigour to the engineers you partner with — review experiment designs, push back on unsupported assumptions, and raise the bar on how the team uses evidence A day in the life You might start the morning defining how the team will measure a new initiative — the success metrics, the counterfactual, the bar for calling it a win. By mid-morning you're with the engineering team turning a proposal into a decision: walking through trade-offs, pushing back where the data doesn't support an assumption. The afternoon is outcome measurement — refining the evaluation pipeline that tracks last week's rollout, updating the CloudWatch dashboard senior leadership uses to gate the next expansion, and prepping the data for an upcoming Director review. About the team We sit inside AWS Networking with a strong Sydney presence and a remit that spans network availability, the data and analytics that support it, and the automation we are building to change how operations are done. You'd be the data science voice in a small, senior team of network and software engineers in Sydney, partnering with the broader network engineering organisation across Seattle and Dublin. Small team, high autonomy, direct line to senior leadership, and a roadmap with real production impact rather than research demos.

Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? We are looking for a Data Scientist who will be responsible to develop scientific solutions to optimize our inbound fulfillment strategy across multiple networks and global regions, to maximize our Customer Experience and minimize our cost to serve and carbon footprint. You will partner with the worldwide scientific community to help design the optimal fulfillment strategy for Amazon. You will also collaborate with technical teams to develop optimization tools for network flow planning and execution systems. Finally, you will also work with business and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, and an ability to influence across business functions at different levels. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with science, business, and technical teams. Key job responsibilities - Design and develop mathematical models to optimize inventory placement and product flows. - Design and develop statistical and optimization models for planning Supply Chain under uncertainty. - Manage several, high impact projects simultaneously. - Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize our Supply Chain. - Communicate data-driven insights and recommendations to diverse senior stakeholders through technical and/or business papers. - Leverage LLMs to improve explainability of our optimization solutions and drive engagement from supply chain planners across the world.

The Amazon Search team creates customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. The Search Relevance team focuses on several technical areas for improving search quality. In this role, you will invent universally applicable signals and algorithms for training machine-learned ranking models. The relevance improvements you make will help millions of customers discover the products they want from a catalog containing millions of products. You will work on problems such as predicting the popularity of new products, developing new ranking features and algorithms that capture unique characteristics, and analyzing the differences in behavior of different categories of customers. The work will span the whole development pipeline, including data analysis, prototyping, A/B testing, and creating production-level components. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world’s leading Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Please visit https://www.amazon.science for more information

Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Applied Scientist in Robot Perception, you will be at the forefront of this transformation. You will develop and deploy state-of-the-art perception algorithms that enable robots to truly understand and interact with the physical world — bridging the gap between theoretical research and real-world impact. Bringing deep expertise in Computer Vision and a nuanced understanding of the capabilities and limitations of modern Vision-Language Models (VLMs), you will innovate boldly and push the boundaries of what's possible. Our vision for the Perception layer is ambitious: to enable seamless, intelligent interaction between the user, the robot, and its environment. This is a rare opportunity to work at the intersection of deep learning, large language models, and robotics — contributing to research that doesn't just advance the field, but reshapes it. You will collaborate with world-class teams pioneering breakthroughs in dexterous manipulation, locomotion, and human-robot interaction, all at an unprecedented scale. Join us in building intelligent robotic systems that will define the future of automation and human-robot collaboration. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions

Our team is involved with pre-silicon design verification for custom IP. A critical requirement of the verification flow is the requirement of legal and realistic stimulus of a custom Machine Learning Accelerator Chip. Content creation is built using formal methods that model legal behavior of the design and then solving the problem to create the specific assembly tests. The entire frame work for creating these custom tests is developed using a SMT solver and custom software code to guide the solution space into templated scenarios. This highly visible and innovative role requires the design of this solving framework and collaborating with design verification engineers, hardware architects and designers to ensure that interesting content can be created for the projects needs. Key job responsibilities Develop an understanding for a custom machine learning instruction set architecture. Model correctness of instruction streams using first order logic. Create custom API's to allow control over scheduling and randomness. Deploy algorithms to ensure concurrent code is safely constructed. Create coverage metrics to ensure solution space coverage. Use novel methods like machine learning to automate content creation. About the team Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for customers who require specialized security solutions for their cloud services. Annapurna Labs (our organization within AWS UC) designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago—even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world.

The Amazon Devices & Services Demand Science (DSci) team is seeking a scientist with strong and AI/ML and communication skills to help with demand forecasting and supply optimization for the entire Amazon device family of products, services, and accessories. Amazon Devices represents a highly complex space with 100+ products across several product categories (e-readers [Kindle], tablets [Fire Tablets], TV [Fire TV and remote], smart speakers and audio assistants [Echo], wifi routers [eero], and video doorbells and cameras [Ring and Blink]), for sales both online and in offline retailers globally. We develop scalable and robust state-of-the-art ML, AI, and automation solutions — including transformer-based forecasting architectures, large language model (LLM)-powered agents, and agentic AI workflows — that learn from diverse data sources and power advanced predictive models. With better forecasts we drive down supply chain costs, enabling the offer of lower prices and better in-stock selection for our customers. In this role, you will own the full lifecycle — from research through production deployment. You will drive end-to-end solutions: understanding business requirements, exploring large-scale historical data and ML models, building prototypes, developing conceptually new approaches (including AI-native experimentation and agent-driven automation), and partnering with engineering teams to deploy and maintain solutions in production. You will collaborate closely with scientists, engineering peers, and business stakeholders. You will be at the heart of a growing and exciting focus area for Amazon Devices and Services where AI is transforming how we forecast, validate, and optimize decisions. You are an individual with strong science abilities, excellent communication skills, solid coding skills, and comfort with modern AI tools and frameworks. You will be responsible for researching, prototyping, experimenting, analyzing predictive models, implementing production-ready solutions, and developing AI-driven automation that progressively reduces manual intervention and enables hands-off-the-wheel science operations. Key job responsibilities - Build forecasting models from prototype through production, working closely with engineering to deploy at scale - Find and integrate new data sources to improve forecast accuracy and coverage - Design and deliver production-ready solutions for business-critical forecasting and optimization problems - Define and track performance metrics — both technical (error rates, bias, coverage) and business (plan attainment, financial impact, reduction in manual overrides) - Write and maintain clear technical documentation; present findings and recommendations to scientists, engineers, and business leaders - Set team standards for methodology, code quality, experimentation rigor, and AI-assisted workflows About the team We are a focused science team looking for help with enhancing the demand forecasting framework we're building and figuring out how to best solve the needs of both our internal stakeholders and cross-functional partners.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. We are seeking a technical leader for our Supply Science team. This team is within the Sponsored Product team, and works on complex engineering, optimization, econometric, and user-experience problems in order to deliver relevant product ads on Amazon search and detail pages world-wide. The team operates with the dual objective of enhancing the experience of Amazon shoppers and enabling the monetization of our online and mobile page properties. Our work spans ML and Data science across predictive modeling, reinforcement learning (Bandits), adaptive experimentation, causal inference, data engineering. Key job responsibilities Search Supply and Experiences, within Sponsored Products, is seeking a Senior Applied Scientist to join a fast growing team with the mandate of creating new ads experience that elevates the shopping experience for our hundreds of millions customers worldwide. We are looking for a top analytical mind capable of understanding our complex ecosystem of advertisers participating in a pay-per-click model– and leveraging this knowledge to help turn the flywheel of the business. As a Senior Applied Scientist on this team you will: --Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects. --Lead technical efforts within this team and across other teams. --Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production. --Run A/B experiments, gather data, and perform statistical analysis. --Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. --Work closely with software engineers to assist in productionizing your ML models. --Research new machine learning approaches. --Recruit Applied Scientists to the team and act as a mentor to other scientists on the team. A day in the life The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and with an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the direction is a must. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to customers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon

Come work with other scientists and software developers in Alexa Smart Home to design and build the next generation of Smart Home experiences using the latest multimodal Large Language Models (LLM's) and Computer Vision. We are evolving Alexa into an intelligent, indispensable companion that automates daily routines, simplifies interaction with appliances, electronics, and cameras and alerts when something unusual is detected. We are focused on making Alexa the user interface for the home, from the simplest voice commands (turn on the lights, turn down the heat) to use cases spanning home security, home entertainment, and the home environment. You can be part of a team delivering features that are highly anticipated by media and well received by our customers. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. As a Senior Applied Scientist, you will work with other scientists and software developers to design and build the next generation of Smart Home control using the latest Large Language Models and Vision Language Models. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. Key job responsibilities - Develop new inference and training techniques to improve the performance of LLM's for Smart Home control and Automation - Solve hard problems in computer vision, video summarization, and video search - Develop robust techniques for synthetic data generation for training large models and maintaining model generalization - Mentoring junior scientists to improve their skills, knowledge, and their ability to get things done About the team We are a team of Scientists, Machine Learning Engineers, and Software Developers that work together to make Alexa more insightful and proactive through ambient intelligence - with features like Alexa Hunches that automatically control Smart Home devices. We are interdisciplinary and we act like it -- we ask each other questions and value our different perspectives.

Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale We are seeking a Postdoctoral Scholar to join the SAF Lab. In this role, you will perform research around safe autonomy on highly dynamic robots, with a special focus on loco-manipulation and dynamically stable robots. This includes, but is not limited to, underlying theory of control barrier functions (CBFs) that enables robust and performant safety on hardware, safe reinforcement learning for agile and robust whole-body control, layered safety filters that interface with learning modules, and the synthesis of CBFs from perception data and semantic information. You will push the boundaries of safe autonomy and validate your discoveries experimentally on the next generation of robotic platforms. The SAF lab provides a unique opportunity to collaborate with the inventor of CBFs, top scientists and engineers at Amazon developing the next generation of safe autonomy, while also establishing strong connections with top academic research labs. Your research in the SAF lab will lay the foundations of safe learning on complex robots – removing bottlenecks to deployment and enable them to safely operate around humans. Key job responsibilities In this role you will: • Push forward the fundamental science of safe autonomy. This can be from a variety of perspectives: theoretic contributions, integration with learning, or synthesis from perception. Especially valuable are methods that bridge these different domains. • Develop the simulation and evaluation pipelines needed to run complex and large-scale validation of methods developed in high fidelity simulation environments. • Develop sim-to-real transfer pipelines that enable the deployment of simulation-based methods (controllers, policies) on hardware. • Deploy the methods developed on hardware, with a focus on dynamically stable robots. Validate the underlying science developed in practice and identify gaps between the science and practice to drive innovation in research. • Publish research at top-tier robotics, control and ML venues and contribute to Amazon's scientific reputation in advanced robotics • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots). A day in the life 0