Careers

You will be working with a unique and gifted team developing exciting products for consumers. The team is a multidisciplinary group of engineers and scientists engaged in a fast paced mission to deliver new products. The team faces a challenging task of balancing cost, schedule, and performance requirements. You should be comfortable collaborating in a fast-paced and often uncertain environment, and contributing to innovative solutions, while demonstrating leadership, technical competence, and meticulousness. Your deliverables will include development of thermal solutions, concept design, feature development, product architecture and system validation through to manufacturing release. You will support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques. Key job responsibilities In this role, you will: - Own thermal design for consumer electronics products at the system level, proposing thermal architecture and aligning with functional leads - Perform CFD simulations using tools such as Star-CCM+ or FloEFD to assess thermal feasibility, identify risks, and propose mitigation options - Generate data processing, statistical analysis, and test automation scripts to improve data consistency, insight quality, and team efficiency - Plan and execute thermal validation activities for devices and SoC packages, including test setup definition, data review, and issue tracking - Work closely with cross-functional and cross-geo teams to support product decisions, generate thermal specifications, and align on thermal requirements - Prepare clear summaries and reports on thermal results, risks, and observations for review by cross-functional leads About the team Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced innovative devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?

Employer: Amazon.com Services LLC Position: Economist III (multiple positions available) Location: Seattle, Washington Multiple Positions Available: 1. Partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond; 2. Build econometric models using our world class data systems and apply approaches from a variety of skillsets - applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon; 3. Work in a fast moving environment to solve business problems as a member of either a crossfunctional team embedded within a business unit or a central science and economics organization; 4. Develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company; and 5. Utilize deep knowledge in time series econometrics, asset pricing, empirical macroeconomics, or the use of micro and panel data to improve and validate traditional aggregative models. (40 hours / week, 8:00am-5:00pm, Salary Range $159,200.00/year to $215,300.00/year) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

Are you passionate about transforming how Amazon identifies and remediates security risks at scale? Does the prospect of using data science and AI to protect Amazon's most critical business operations excite you? Stores BST Risk Engineering builds data-driven risk intelligence solutions that help security teams across Amazon's stores identify, prioritize, and remediate strategic security risks. Our customers range from security engineers managing thousands of vulnerabilities to business leaders making strategic risk decisions. We use machine learning, generative AI, and large-scale data analysis to solve diverse security risks at a very large scale. Your responsibilities will include developing machine learning models for automated fraud detection and pattern recognition, building clustering algorithms that identify root causes across thousands of security issues, and creating data correlation pipelines that integrate security signals from multiple sources. You'll use advanced AI and Data Science techniques for fraud detection, unsupervised learning for pattern discovery, and generative AI for automated analysis at scale. Your work will directly enable security teams to shift from manual analysis taking weeks to automated insights delivered in minutes. We're looking for data scientists who can prototype and productionize innovative models using supervised, unsupervised, and reinforcement learning techniques. You'll work on problems like correlating external threat intelligence with asset inventories, building statistical profiles to detect evolving fraud patterns, and designing confidence scoring systems for real-time risk categorization. If you want to apply machine learning to never-before-solved security problems at Amazon scale, this is your team. Key job responsibilities - Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard AI/ML models and working with Large Language Models - With your broad expertise in a variety of data science disciplines, recommend the right data science strategy and drive solution to complex or ambiguous problems - Work closely with internal stakeholders like the business teams, engineering teams and partner teams, influence their strategies to align with your focus area - Innovate by adapting new modeling techniques and procedures - Passionate about working with huge data sets ( training/fine tuning) and be someone who loves to bring datasets together to answer business questions. You should have deep expertise in creation and management of datasets - Exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive. - Show good judgment when making trade-offs between short-term customer, market, or research needs and long-term operations or technology needs. About the team About Amazon Security Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

The R2L team is responsible for building the next generation supply chain for Amazon’s world-class ultra-fast customer experiences including Amazon Fresh groceries, Sub-Same Day, Amazon Now, and other soon-to-launch exciting new businesses. Join us and you'll be taking part in serving our customers in as fast as 30 minutes! We are looking for a Data Scientist to join our team and solve some of the most complex business problems! Key job responsibilities - Work with product managers, engineers, other scientists, and leadership to identify and prioritize complex problems. - Interview stakeholders to gather business requirements and translate business problems into data science or analytical problems - Design, develop, and evaluate highly innovative statistics and ML models - Guide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Proactively seek to identify business opportunities and insights and provide solutions to shape key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams. A day in the life In this role, you will be a technical expert with significant scope and impact. You will work with Product and Supply Chain Managers, Business Intelligence Engineers, System Developers and other Scientists, to develop models that solve a wide range of complex and ambiguous business problems, with the main goal to improve customer experience, improve availability of products and reduce supply chain cost. A successful Data Scientist will have bias for action needed in a startup environment, with great leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. It will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term scientific solutions. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team The R2L team is responsible for building the next generation supply chain for Amazon’s world-class ultra-fast customer experiences including Amazon Fresh groceries, Sub-Same Day, Amazon Now, and other soon-to-launch exciting new businesses. Join us and you'll be taking part in serving our customers in as fast as 30 minutes!

Amazon is looking for creative Applied Scientists to tackle some of the most interesting problems in Artificial Intelligence (AI), natural language processing (NLP), knowledge graph curation, search, recommendation and related areas with our Amazon Books team. At Amazon Books we believe that reading is essential for a healthy society. As such, we aim to inspire readers by making it easy to read more and get more out of reading. We do this by creating an unmatched book discovery experience for our customers worldwide. We enable customers to discover new books, authors and genres through smart search tools, intelligent interactions and sophisticated recommendations, and we need your help to keep innovating in this space. If you are looking for an opportunity to solve deep technical problems and build innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you. You will develop and implement novel algorithms, agentic systems and modeling techniques to advance the state-of-the-art in technology areas at the intersection of AI, LLMs, NLP, search, and deep learning. You will innovate at scale, help move the needle for research in these exciting areas and build state-of-the-art technologies that enable delightful experiences for hundreds of millions of people. Key job responsibilities In this role you will: - Work collaboratively with other scientists and developers to design and implement scalable and reliable pipelines for ensuring our vast knowledge base of books meets the highest levels of accuracy and is aligned with how customers think about books; - set the scientific roadmap needed to sustain state-of-the-art quality and efficiency. Stay up-to-date with the latest technologies and adopt the most appropriate long-term solution to a variety of use cases. - set the standard for science rigor and quality, deal with ambiguity and competing objectives, and mentor other members to achieve their career growth potential. - Drive scalable solutions: from understanding the customer and business needs, to prototyping, production testing and through engineering directly to production. A day in the life Day-to-day work varies, but on a typical day you will: - run (or supervise) experiments. Depending on the stage or project, this may involve designing the experiments, collecting data, training models, prompt/spec engineering/optimization, error analysis, online system analysis, etc. - drive decisions by interpreting and communicating experimental results with stakeholders (managers, subject matter experts, other scientists, and engineers) and seeking/listening to their feedback. - strategic thinking: review results, identify trends and what step-changes are needed. You'll contribute to regular meetings, activities and conferences with the wider science team, organization as well as internal and external communities. About the team The team consists of a collaborative group of scientists, product leaders, and dedicated engineering teams. Our aim is to maintain the world’s most accurate and descriptive set of books metadata, where every title in our catalog is uniquely characterized via a set of high-quality, concise attributes. We believe this is a foundational capacity for any bookstore. We work with sister teams to leverage our systems to drive a diverse array of customer experiences, owned both by ourselves and others, that enable customers to easily identify their ideal next read.

Lead ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Science Manager, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.

Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create 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. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK). Please note these are not remote internships.

This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.