Careers

Amazon’s Last Mile Delivery organization is responsible for the on-time and error-free delivery of tens of billions of packages annually, in 20+ countries worldwide. The organization’s focus over the years has expanded beyond package delivery to include groceries and heavy and bulky items and has dramatically increased the speed of delivery from two day, to one day, to sub-same day for millions of items, a trend that will continue with quick commerce deliveries within minutes. Underpinning this massive delivery logistics operation (one of the largest in the world) is innovative technology leveraging state of the art AI and ML solutions developed by the Geospatial Science team, one of the largest science teams within the Amazon Operations organization. The Geospatial Science team is responsible for the quality and coverage of the core geospatial data, solvers, and real-time workflows that operate over petabytes of data, power trillions of transit time calculations daily, and operate on diverse environments spanning multi-modal cloud-based learning workflows, highly throughput and low-latency services, and edge compute applications on smart phones, delivery vehicles, and delivery stations. Geospatial Science capabilities operate at the critical path a broad array of mission critical workflows ranging from customer address creation, order placement, delivery route planning, delivery route execution, and package drop-off. The Director, Applied Science (Geospatial) owns the end-to-end science portfolio that enables these capabilities by leveraging innovative AI and ML techniques. They are responsible for (1) learning and improving a worldwide catalog of addresses with high-quality validation and geo-resolution, (2) building a places dataset to model where we delivery ranging from every single single-family home, campus, building, and apartment - along with their relationships and delivery critical attributes such as delivery hours, access information, mail rooms, delivery lockers, parking locations, entrances, and drop-off geocodes, (3) developing maps that capture a fresh and accurate road network, enable precise transit paths that optimize travel times while reducing travel risk in delivery routes and on-road navigation experiences and (4) developing feedback loops that leverage edge capabilities of millions of smart phones and tens of thousands of delivery vehicles to capture fresh street imagery, learn street signs, road markings, and road obstructions at scale, and reconstruct key delivery events and activities to improve the fidelity of address, place, and road datasets, optimize routes, and reduce defects. This leader will lead a worldwide team of approximately 50 scientists, with expertise in generative AI, computer vision, and machine learning. This leader requires broad and deep skills in innovative AI and ML techniques to take advantage of the latest advances in the field. A key focus is accelerating the development and adoption of GenAI-based solutions, in the face of rapid shifts in the science and technology landscape, by guiding the team to maximize the value that can be delivered using latest LLMs, VLMs, agentic paradigms, and reasoning agents. Computer vision based solutions form an important part of the portfolio, as the team innovates on scaled inputs like satellite, aerial, and camera imagery for many problems, such as road learning and transporter safety. This leader will be expected to invest in research and innovation to deliver novel solutions to unlock new opportunities to grow the business while making pragmatic tradeoffs to deliver timely customer value, in conjunction with product, engineering, and operational leaders and teams. This leader will be expected to interface with senior leaders (up to SVP) and senior partners and stakeholders across the World-Wide Operations organization and Amazon. Day-to-day interactions will span product partners with whom s/he will design end-to-end customer solutions and long-term product plans and strategies, engineering partners with whom s/he will execute the development and productization of multi-modal workflows and solvers, and multi-disciplinary upstream and downstream stakeholders and partner teams. This leader will be expected to co-own yearly and 3-year planning documents for the Geospatial Technology space. S/he will also be expected to build and demonstrate advanced research prototypes and proposals up to the SVP level. S/he will be expected to recruit senior scientists and science leaders and managers for their own team as well as other peer teams across Amazon. S/he will need build and maintain a high-performing team and develop and promote scientists and science leaders (up to Principal/Sr Manager/Sr Principal). Key job responsibilities - Lead a worldwide team of scientists to develop and deploy AI and ML solutions for geospatial problems to accelerate and optimize Amazon's global delivery operations - Interface with senior stakeholders across engineering, product, and operations teams to design end-to-end solutions, execute model delivery to production, and drive shared goals - Contribute to strategic planning by developing yearly and 3-year planning documents - Present to senior executives (VPs) and stakeholders via demo sessions, science reports, and quarterly business reports - Drive innovation by leveraging SOTA scientific techniques ranging from GenAI (LLMs/VLMs/agents), computer vision, and traditional ML to solve delivery-related problems - Build organizational capability by recruiting and promoting senior scientists and science leaders and maintain a high-performing team

Join the AWS Perimeter Protection team as an Applied Scientist, where you will design and build AI/ML models that protect AWS customers from cyber threats at massive scale. You will work on challenging problems in threat detection, bot management, DDoS protection, and web application security — developing and deploying machine learning solutions that leverage techniques including large language models, generative AI, and agentic AI systems. Operating across all AWS regions and processing trillions of requests per week, you will collaborate with experienced scientists and engineers to deliver production-grade, intelligent security systems that provide robust, adaptive, and forward-looking protection for AWS customers worldwide. Key job responsibilities - Design, develop, and evaluate ML models and algorithms for threat detection, anomaly detection, and mitigation of evolving cyber threats including DDoS attacks, bot activity, and web application exploits. - Explore and apply large language models, generative AI, and agentic AI approaches to security challenges such as automated threat analysis, intelligent mitigation, and adaptive defense systems. - Implement end-to-end ML solutions — from data exploration and feature engineering through model training, evaluation, and deployment into production systems. - Analyze large-scale datasets to uncover patterns, identify emerging threat vectors, and translate findings into effective ML-based security solutions. - Build and maintain data pipelines and model training workflows that support rapid experimentation and reliable production performance. - Collaborate with software engineers to integrate ML models into low-latency, high-throughput security systems at cloud scale. - Design and run experiments to validate model performance, measure impact, and iterate on approaches using rigorous scientific methodology. - Stay current with recent advances in AI/ML — including LLMs, generative AI, and agentic systems — and cybersecurity research, applying relevant techniques to improve detection and protection capabilities. - Contribute to design reviews, and knowledge sharing. - Participate in the team's scientific roadmap by proposing ideas and identifying opportunities to improve existing systems.

The AWS Neuron Science Team is looking for talented scientists to enhance our software stack, accelerating customer adoption of Trainium and Inferentia accelerators. In this role, you will work directly with external and internal customers to identify key adoption barriers and optimization opportunities. You'll collaborate closely with our engineering teams to implement innovative solutions and engage with academic and research communities to advance state-of-the-art ML systems. As part of a strategic growth area for AWS, you'll work alongside distinguished engineers and scientists in an exciting and impactful environment. We actively work on these areas: - AI for Systems: Developing and applying ML/RL approaches for kernel/code generation and optimization - Machine Learning Compiler: Creating advanced compiler techniques for ML workloads - System Robustness: Building tools for accuracy and reliability validation - Efficient Kernel Development: Designing high-performance kernels optimized for our ML accelerator architectures A day in the life AWS Utility Computing (UC) provides 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, Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Additionally, this role may involve exposure to and experience with Amazon's growing suite of generative AI services and other cloud computing offerings across the AWS portfolio. About the team AWS Neuron is the software of Trainium and Inferentia, the AWS Machine Learning chips. Inferentia delivers best-in-class ML inference performance at the lowest cost in the cloud to our AWS customers. Trainium is designed to deliver the best-in-class ML training performance at the lowest training cost in the cloud, and it’s all being enabled by AWS Neuron. Neuron is a Software that include ML compiler and native integration into popular ML frameworks. Our products are being used at scale with external customers like Anthropic and Databricks as well as internal customers like Alexa, Amazon Bedrocks, Amazon Robotics, Amazon Ads, Amazon Rekognition and many more.

The Quantum Algorithms Team at the Amazon Web Services (AWS) Center for Quantum Computing (CQC) is looking to hire an Applied Scientist. In this role, you will conduct theoretical research into applications of fault-tolerant quantum computers and develop new quantum algorithms. You will work on the forefront of quantum computing research and contribute toward the development of useful quantum technology, working alongside theoretical and experimental scientists, engineers, and technicians across the CQC. You should have a deep and broad knowledge of quantum algorithms and applications for fault-tolerant quantum computers, and standard mathematical and computational methods for analyzing them. Key job responsibilities We are looking to hire an Applied Scientist focused on developing algorithms and applications for fault-tolerant quantum computers. In this role, you will: - Collaborate with other members of the Quantum Algorithms Team and our academic collaborators, to analyze and optimize existing quantum algorithms - Think big to invent new quantum algorithms and discover new application areas for fault-tolerant quantum computers - Provide subject matter expertise on the topic of quantum algorithms, and communicate the latest developments to the broader quantum computing team - Work with internal and external stakeholders to strengthen the long-term value proposition of quantum computing - Interact with the Quantum Error Correction team to understand the interplay between quantum algorithms and quantum error correction - Publish research papers in scientific journals and present at conferences - Mentor research interns working on scientific projects You are expected to develop and lead high-impact research projects that intersect with our engineering roadmap. Organization and communication skills are essential. A day in the life As an Applied Scientist on the Quantum Algorithms team, you develop practical applications for quantum computers. To do so, you will: - Read academic publications, understand their practical applicability, and present the results in internal meetings - Analyze and optimize existing quantum algorithms in domains such as quantum simulation, quantum chemistry, optimization, or machine learning - or develop entirely new algorithms - Perform resource estimates to quantify the cost of running quantum algorithms within a given hardware architecture - Write technical internal reports documenting research findings and analysis - Write research papers and present your research results at flagship academic and industry conferences.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Our products are used daily to surface new selection and provide customers a wider set of product choices along their shopping journeys. The business is focused on generating value for shoppers as well as advertisers. Our team uses a combination of econometrics, machine learning, and data science to build disruptive products for all our Advertising products. We also generate insights to guide Amazon Advertising strategy, providing direct support to senior leadership. We are looking for an experienced Applied Scientists who have a deep passion for building machine-learning solutions, ability to communicate data insights and scientific vision, and execute strategic projects. As an Applied Scientist on this team, you will: • Build full life-cycle machine learning solutions; build models and perform data analysis to deliver scalable solutions to business problems. • Scale ad performance insights through agentic systems/LLMs. • Perform hands-on analysis and modeling with enormous data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience. • Work closely with software engineers on detailed requirements to productionize the ML models you build. • Run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research innovative machine learning approaches. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. BASIC QUALIFICATIONS • PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field • 3+ years of experience of building machine learning models for business application • Experience programming in Python or related language

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. The Sponsored Brands team at Amazon is building the next generation of brand advertising products and platforms for advertisers using Gen AI. This role presents an opportunity to join us at the ground floor of this transformation, which is a core part of Amazon's overall business strategy. You will be the Gen AI applied science leader that will determine what products and ads show up in the most critical ad placements across Amazon. You'll have opportunities to deliver at the highest technical level, working with science, product, engineering, and UX, all directly connected to a marketplace of supply and demand. In this highly visible role, you'll collaborate across multiple stakeholders within Ads and Retail. We are seeking a Gen AI Applied Science inventor who has strong product sense, with a proven track record with hands on science vision and execution while building high impact products from the ground up. you will have the opportunity to apply your deep subject matter expertise in the area of ML, LLM and GenAI models. You will invent new product experiences that enable novel advertiser and shopper experiences. This role will work on bringing state-of-the-art GenAI models to production. You will define the long-term science vision for our advertising business, driven by our customer’s needs, and translate it into actionable plans for our team of applied scientists and engineers. Key job responsibilities You will play a pivotal role in managing projects at all stages: inception, design, development, deployment and subsequent improvements. You will tackle challenging problems and coordinate high profile projects across multiple teams to ensure customer and business goals are met. You will interface across product, science, and engineering teams within the advertising and retail organizations. You will drive mechanisms that allow the team to move quickly and deliver strong results. You will operate with a high degree of autonomy and efficiency. You'll be responsible for project management - prioritize, plan projects and features, manage partners, and track external commitments. You will recommend alternative technical approaches and partner with product, science, and engineering teams to meet timelines. You will actively role model the use of GenAI to make the team more efficient. About the team We are on a mission to make Amazon the best in class destination for shoppers to discover, engage, and purchase relevant products, from brands that are relevant to them. In this role, you will design and implement Gen AI solutions that help millions of advertisers create more effective ad campaigns with intelligent recommendations, while improving the overall experience at Amazon's global scale. Our team invents, defines, and delivers advertising products that drive brand discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon Store businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, fast-paced, and collaborative team with an entrepreneurial spirit.

Every time a customer clicks "Add to Cart" on Amazon, they're trusting us to have already answered a deceptively hard question: out of hundreds of competing offers for the same product, which one is actually the best choice for them right now? Our team owns the algorithm that answers that question — billions of times a day, across every product on Amazon. As a Data Scientist on this team, you'll help make that algorithm smarter. You'll work on the models and signals that rank and surface offers in real time — weighing price, seller performance, fulfillment speed, customer trust signals, and more. The work is technically deep: you're not running one-off analyses, you're building systems that operate at internet scale and directly affect what customers buy and how sellers compete. You'll run experiments, develop predictive models, and partner with engineers and product managers to turn your findings into features that ship. If you enjoy problems where getting the answer right really matters — for customers, for sellers, and for the business — you'll find a lot to work on here. Key job responsibilities Build and own machine learning models that rank and recommend offers to customers across Amazon's product catalog, from feature engineering through production deployment Design and analyze A/B experiments to measure the impact of algorithm changes on customer experience, seller competition, and business outcomes Mine large-scale datasets to identify patterns and signals — seller behavior, pricing dynamics, fulfillment performance — that improve how we predict the best offer for each customer Translate ambiguous business questions into well-defined data science problems, and communicate findings clearly to engineers, product managers, and leadership Partner with software engineers to operationalize models at scale, ensuring they perform reliably under high-traffic, low-latency conditions Monitor model performance over time, diagnose degradation, and iterate to keep ranking quality high as the marketplace evolves A day in the life You'll spend your time building and iterating on models that rank offers for hundreds of millions of Amazon shoppers — checking experiment results, digging into datasets to find better signals, and working with engineers to get models into production. Day to day, you'll partner with product managers and engineers to turn business questions into data science problems: Why is this ranking signal underperforming? What's driving a shift in customer behavior? Is this change actually better for customers? Your work directly shapes what customers see and how sellers compete — at a scale few roles can match. About the team We own one of the most consequential algorithms at Amazon — the system that decides which offer a customer sees when they're ready to buy. It's a small surface area with enormous impact, and we take that responsibility seriously. Our team brings together scientists, engineers, and product managers who are genuinely obsessed with getting the ranking right — for customers and for sellers. We move fast, run lots of experiments, and debate ideas openly. We care about doing the science rigorously, shipping things that matter, and not taking shortcuts that erode customer trust. If you like working on hard problems with people who hold each other to a high bar, you'll fit in here.

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers

Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. For more information on the Amazon Science community please visit https://www.amazon.science.