Careers

Amazon.com’s Product Detail Page team is looking for talented, motivated and passionate applied scientist to be part of the design and development of a highly scalable multi-tiered shopping application to provide the best possible online shopping experience for Amazon customers world-wide. Our team is comprised of talented applied scientists, developers, testers, program managers, designers and product managers tasked with the singular goal to create THE world's best buying experience. Scientists on this team develop the next-generation technologies and experiences that change how millions interact and shop online. To provide the best possible online shopping at the scale of the web requires ideas from every area of computer science, including distributed computing, large-scale system design, machine learning, natural language processing, data compression and user interface design; the list goes on and is growing every day. We need our scientists to be versatile and always eager to tackle new problems as we continue to push technology forward. Our team leverages sophisticated econometric, machine learning, and big data technologies to help customers to discover the right products at the right prices from millions of trusted sellers billions of times a day. If you are looking for a career-defining opportunity on one of the most customer centric and business impacting teams within Amazon, we’d love to hear from you. We are looking for an Applied Scientist to help build the next generation of Detail Page optimization algorithms. These new set of algorithms will incorporate the continually changing preferences of our customers and continue to scale with numerous new programs that Amazon is introducing for our customers. You will work with multiple Amazon businesses and programs to identify big business opportunities and propose new business features and technical systems to improve customer experience on Amazon Detail Page, Search Page and many other widgets throughout the website. You will be responsible for the quality of algorithm design and will get the opportunity to present your ideas and share results of your deliverables with Amazon executives on a frequent basis. You will get an opportunity to work with senior scientists to define and enforce broad, company-wide technical standards in optimization techniques, statistical modeling and simulation techniques, and/or data analytics.

At Amazon, our SCOT Labs team owns and operates the experimentation platform that powers randomized controlled trials (RCTs) across Supply Chain Optimization Technologies (SCOT). We are the scientific gatekeepers for policy updates that govern how Amazon buys, stores, and moves billions of units of inventory worldwide. This is not traditional A/B testing: we are building the infrastructure and methodology to causally evaluate complex and interconnected supply chain interventions. Our platform runs experiments that span millions of products and hundreds of fulfillment nodes simultaneously, measuring the real-world impact of policy changes on inventory health, customer experience, and operational cost. We are also advancing the science of causal inference in supply chain settings by developing novel approaches to treatment effect estimation, interference modeling, and emulation techniques that allow us to assess policy impact faster and more accurately than ever before. The experiments you design and the methods you build here will directly determine which policies ship to production. These decisions influence hundreds of millions of dollars in weekly inventory investments, labor allocation for tens of thousands of associates, and Amazon's overall supply chain efficiency. Beyond operational impact, this team pushes the frontier of causal experimentation methodology and contributes to the broader scientific community with publications at top venues. If you are a scientist who wants to shape how one of the world's largest supply chains makes decisions — solving causal inference challenges in real-world settings no academic lab or startup can replicate — this is the team for you. Key job responsibilities - Partner with customer teams to design rigorous large-scale experiments (such as randomized controlled trials and quasi-experiments) to evaluate policy updates and model improvements across millions of products, hundreds of fulfillment nodes, and diverse business contexts - Build and maintain production-grade experimentation infrastructure and analytical tools using Python, SQL, Scala, and related technologies - Advance causal inference methodology for supply chain settings, including treatment effect estimation, interference modeling, and emulation techniques that accelerate policy evaluation. Ensure these methods are supported by both theoretical foundations and empirical evidence. - Translate complex research findings into clear insights and actionable recommendations for technical and non-technical stakeholders at all levels. - Contribute to Amazon's scientific community and the broader external research field through collaboration and publication in top-tier venues. A day in the life You might start the morning digging into results from a randomized trial running across millions of products, deciding whether the estimator you built last month is holding up and where it breaks. By the afternoon you could be designing the next experiment with a partner team where clean randomization is not available, because treated and control units draw on the same fulfillment network and inventory pools, and you need an approach that stays credible despite the spillover. The role sits between developing methods and shipping them. You will build estimators and experiment designs for problems without textbook answers, prototype them in Python on real supply chain experiment data, and then put the ones that work into the platform other SCOT scientists use, writing Python, Scala, and SQL at a scale most scientists never encounter. You will also build supply chain emulation systems that replicate inventory dynamics end to end, so a design can be stress tested before it touches the real network. The problems are open, the data is unmatched in scale, and the methods you build will shape how scientists across SCOT think about causal inference and move billions of dollars in inventory decisions. If you want to do measurement and experimentation research that actually ships, this is where you do it. About the team The Forecasting and Labs Science team sits at the heart of Amazon's supply chain, building the science that determines what products are available, when, and at what cost for hundreds of millions of customers around the world. Our mission spans two deeply connected frontiers: pushing the boundaries of large-scale time series forecasting through foundation models that generalize across an enormous and diverse catalog of products, and building the experimentation and causal inference methodology that rigorously evaluates whether supply chain policy changes should ship to production. We are a team of scientists who care deeply about both research rigor and real-world outcomes. We don't just publish: we ship. And we don't just ship: we measure, iterate, and raise the bar. On the forecasting side, we build foundation models at a scale unmatched in industry, running experiments across millions of products and exploring novel data generation techniques that open new frontiers in model generalization. On the experimentation side, we design and run randomized controlled trials across hundreds of fulfillment nodes, advance causal inference in settings where interference is unavoidable, and build supply chain emulation systems that can evaluate policy changes in hours rather than months. Our work spans the full lifecycle: from foundational research and large-scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.

We are the Audible Research team and our goal is to improve the core technology to improve Alexa's voice and make it more human-like and expressive. We are looking for a passionate, talented, and inventive Scientist with a strong background in Machine/Deep Learning to join us in beautiful Cambridge, UK. Our mission is to push the envelope in computer-generated speech in order to provide the best-possible experience for our customers As an applied scientist, you will work with talented peers to develop novel algorithms and modelling techniques to advance the state-of-the-art in spoken language generation. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in computer generated speech. Position Responsibilities: - Research and implement novel Machine/Deep Learning approaches which add value to Amazon - Lead and Mentor junior engineers and scientists - Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for spoken language applications - Develop and/or apply statistical modelling methods (e.g. deep neural networks), optimizations, and other ML techniques to different applications in spoken language engineering

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge 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 Products Search Sourcing Science (SPSSS) team's mission is to retrieve all relevant sponsored products in response to shopper queries, serving billions of daily ad impressions and tens of millions of clicks, helping shoppers discover useful and contextually relevant products while enabling advertisers to reach the right shoppers in the right context. To achieve this, we build state-of-the-art capabilities spanning query, shopper, product, and advertiser understanding, as well as advanced retrieval, targeting, and ranking systems, all powered by efficient large-scale data pipelines, deep learning, natural language processing (NLP), generative AI, and multi-agent workflows. It's a high-impact, technically exciting space where science directly translates into measurable outcomes for hundreds of millions of customers and millions of advertisers. Key job responsibilities As a Senior Applied Scientist on this team, you will: - Serve as the technical leader in Machine Learning and Generative AI, driving efforts within this team and across other teams. - Lead end-to-end ML projects with high ambiguity, scale, and complexity—from problem definition to production. - Build, optimize, and deploy ML models into production, partnering with software engineers to productionize solutions. - Establish scalable, automated processes for data analysis, model development, validation, and serving. - Apply strong knowledge of LLMs (prompt engineering, fine-tuning, RAG, evaluation) to build production-grade GenAI applications. - Analyze large-scale data sets to develop insights that increase traffic monetization and merchandise sales without compromising the shopper experience. - Design and run A/B experiments, and perform statistical analysis to measure impact and guide decisions. - Research and prototype innovative ML and GenAI approaches, bringing state-of-the-art techniques into production. - Recruit, mentor, and grow Applied Scientists on the team. About the team 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. The Sponsored Products Search Sourcing Science (SPSSS) team's mission is to retrieve all relevant sponsored products in response to shopper queries, serving billions of daily ad impressions and tens of millions of clicks, helping shoppers discover useful and contextually relevant products while enabling advertisers to reach the right shoppers in the right context. To achieve this, we build state-of-the-art capabilities spanning query, shopper, product, and advertiser understanding, as well as advanced retrieval, targeting, and ranking systems, all powered by efficient large-scale data pipelines, deep learning, natural language processing (NLP), generative AI, and multi-agent workflows. It's a high-impact, technically exciting space where science directly translates into measurable outcomes for hundreds of millions of customers and millions of advertisers.

Do you want to solve unique challenges specific to the Canadian market? Are you looking to make an impact on a digital commerce landscape through science? We are hiring an Applied Scientist to join Amazon Canada Technology and push the science boundaries for Canada: the reasoning LLMs, embedding models, cross-encoder rankers, and multi-objective optimization systems that turn billions of products into the right answer for hundreds of millions of Canadian customers. The role spans the full model lifecycle, from understanding and deciphering where the problem lies, building models, mid-training reasoning models on data such as shopping, returns, reasoning, localization to aligning the models with customers on dimensions that matter for shopping through fulfillment. You will be a pioneer on this team and will own defining the long-term Science strategy and execute on the same by partnering closely with several partner teams such as Core Search, Returns and work with CA Product Managers, Software Engineering and BI/DEs. Key job responsibilities • Develop personalized multi-modal thinking-LLM techniques that reason about customers, search, queries, ranking and products across both primary and secondary languages. • Train large language models on various data signals and sets: domain-adaptive continued pre-training, reinforcement learning shopping reasoning traces, instruction tuning for natural-language shopping queries. • Analyze Canada (CA) ranking-quality gaps using offline metrics and benchmark CA model staleness and architecture against other marketplace (e.g. US, EU, JP) equivalents. • Formulate falsifiable ranking hypotheses linking a specific feature, architecture, or training change to predicted metric movement. • Run standardized offline evaluation of model candidates; validate feature statistical distributions and coverage in-locale to detect anomalies; own and lead experiments to drive improvements on offline and online experiment benchmarks. • Lead error analysis on failing query segments and segment-level deep dives (head/torso/tail, category, device), surfacing CA-specific failure modes such as bilingual and thin-category gaps. • Own metric-tradeoff analysis and acceptable-regression boundaries, bridging model behavior and CA business priorities. • Partner with teams such as Core Search on hypotheses, model training, and quality gates (MTBR / WLBR / SSAI). • Classify feature-expansion effort tiers (available / pipeline extension / locale training / infeasible) combining locale knowledge with science judgment. About the team Amazon.ca Technology builds the products and services millions of Canadians rely on every day. Our teams tackle challenges unique to the Canadian market while contributing to Amazon initiatives that span the globe — giving you the best of both worlds: meaningful local impact and the scale of global tech. If you want to help shape Canada's digital commerce landscape within a collaborative Canadian work culture, this is the place to do it.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist III Job Location: Culver City, California Job Number: AMZ10564141 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. 40 hours / week, 8:00am-5:00pm, Salary Range: $167,100/year to $226,100/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

Have you ever wondered how that Amazon box with the smile arrives so quickly, where it came from, and how much it cost Amazon to deliver? The WW Amazon Logistics, Business Analytics team manages the delivery of tens of millions of products every week to Amazon's customers, achieving on-time delivery in a cost-effective manner. We are seeking an enthusiastic, customer-obsessed Research Science Manager(RSM) with strong analytical skills to join our team. This role is crucial in optimizing Amazon's vast delivery network and will have significant impact on the customer experience, particularly in the final phase of delivery. As a Research Science Manager, you will: 1. Address business challenges through building compelling cases and using data to influence change across the organization 2. Develop input and assumptions based on preexisting models to estimate costs and savings opportunities associated with varying levels of network growth and operations 3. Create metrics to measure business performance, identify root causes and trends, and prescribe action plans 4. Manage multiple high-impact projects simultaneously 5. Work with technology teams and product managers to develop new tools and systems supporting business growth 6. Communicate with and support various internal stakeholders and external audiences 7. Implement scheduling solutions, improve metrics, and develop scalable processes and tools Looking for minds with: Extensive experience in operations research and data-driven decision making Strong analytical and problem-solving skills Robust program management and research science skills Ability to work with a team and make independent decisions in ambiguous environments Customer-obsessed mindset with a focus on improving the Amazon delivery experience This role offers the autonomy to think strategically and make data-driven decisions from day one. Join us in shaping the future of e-commerce delivery and addressing the core challenges in our world-class operations space! Key job responsibilities Key job responsibilities 1. Advanced Modeling and Algorithm Development: Design and implement sophisticated machine learning models for logistics optimization Develop complex time series forecasting algorithms for demand prediction and resource allocation 2. AI and Machine Learning Integration: Architect and deploy AI-powered systems to enhance decision-making in logistics operations Implement deep learning techniques for image recognition in package sorting and handling Develop reinforcement learning algorithms for adaptive scheduling and resource management 3. Big Data Analytics and Processing: Design and implement distributed computing solutions for processing massive logistics datasets Utilize cloud computing platforms (e.g., AWS) for scalable data processing and analysis 4. AI-Driven Workflow Optimization: Design and implement AI agents for autonomous decision-making in logistics processes Create machine learning models for customer behavior analysis and personalized delivery options 5. Software Development and System Architecture: Write efficient, scalable code in languages such as Python, Java, or C++ Develop and maintain complex software systems for logistics optimization Stay at the forefront of AI and ML research Publish research findings in top-tier conferences and journals About the team We are Amazon's Last Mile Science and Analytics team, dedicated to improving e-commerce delivery. We work to optimize our vast network, forecast demand using machine learning, and enhance route efficiency. Our efforts focus on developing innovative delivery methods, applying AI to solve complex problems, and conducting geospatial analysis. We create simulations to refine processes and plan capacity effectively. Operating globally, we strive to develop adaptable solutions for diverse markets. We aim to advance logistics science, continually improving speed, efficiency, and customer satisfaction, in support of Amazon's mission to be Earth's most customer-centric company.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. Key job responsibilities • Design, develop, and deploy automated laboratory workflows on liquid-handling platforms, from initial concept through validation and production release. • Partner directly with scientists and stakeholders to gather requirements, understand experimental goals, and translate ambiguous or loosely defined needs into robust, scalable automation solutions. • Perform data quality control (QC), document results, and clearly communicate outcomes to both technical and non-technical stakeholders. • Develop and integrate instrument, software, and data systems, supporting end-to-end implementation of automated workflows. • Identify, troubleshoot, and escalate equipment malfunctions, resolving common errors and minimizing workflow downtime.

Fauna Robotics is building capable, safe, and delightful robots for everyday life, and voice is one of the most natural ways people will interact with them. Cloud speech and language models are good and getting better, but they can only work with the audio they receive, and a robot is a hard place to listen. Its microphones sit beside motors, fans, and moving joints. It speaks through its own loudspeaker while people talk over it. It moves, turns, and shares a room with several people at once. We are hiring a Principal Audio Scientist to be Fauna's technical authority on how our robots hear. You will design, prototype, and ship the hardest algorithms in the robot's audio system. You will set the audio architecture that other engineers build on, shape hardware decisions across robot generations, mentor the engineers and scientists working on audio, and be the person teams come to when the robot can't hear. Key job responsibilities - Set the long-range science roadmap and technical architecture for the robot's audio system, and serve as Fauna's primary technical authority on robot hearing - Design and implement suppression of the robot's own noise from motors, fans, and moving joints - Design and implement echo cancellation for the robot's own voice, so people can interrupt it naturally - Develop multi-microphone processing that holds up as the robot and the people around it move, including locating who is speaking so the robot can turn toward them - Make on-robot listening decisions robust to internal and external noise sources: wake word, voice activity, and whether speech is directed at the robot - Drive microphone and speaker placement, enclosure acoustics, and vibration isolation decisions with mechanical, electrical, and industrial design, backed by your own measurements - Design the robot-specific data collection and evaluation methods to validate the performance of our audio design - Present audio science and its tradeoffs to senior leadership and partner teams - Mentor scientists and engineers, raising the scientific bar for audio across the organization through design reviews, code reviews, and hiring

We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments. Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors - Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency - Use simulation and real-world testing to refine and validate control algorithms - Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you. an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.