Careers

We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!

Are you interested in how to build AI reasoning systems that give provably correct answers? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve operations customers better? Amazon Robotics is looking for a talented Applied Scientist in Neurosymbolic AI. You will innovate on combining language models (LMs) with classical AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LMs to generate plans, using AI reasoning to verify plan correctness, learning efficient reasoning strategies, self-improving models. You will work on basic science and on business problems in robotics, automation and fulfillment across our operations. Key job responsibilities In this role you will: • Work closely with other scientists and engineers, and be part of Amazon’s diverse global science community. • Publish your research in top-tier academic venues and hone your presentation skills. • Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise. A day in the life You'll meet regularly with your technical lead and your team on your ideas, get guidance and feedback, work together on architectures and algorithms, author papers, build AI systems, all with the aim of delivering results for your operations customers. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement your ideas at scale. About the team The Veritas team is a science team working at the boundary between language models and classical AI reasoning. We work across on customer problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.

As a Applied Scientist, you will be responsible for assessing and optimizing the thermal performance of our new and emerging category of devices - Amazon Leo customer terminals. AMZ Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to un-served and under-served communities around the world. 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: - Evaluate and optimize thermal solution requirements of consumer electronic products - Use simulation tools like Star CCM/FloTherm XT/EFD for analysis and design of products - Validate design modifications for thermal concerns using simulation and actual prototypes - Establish temperature thresholds for user comfort level and component level considering reliability requirements - Have intimate knowledge of various materials and heat spreaders solutions to resolve thermal issues - Use of programming languages like Python and Matlab for analytical/statistical analyses and automation - Collaborate as part of device team to iterate and optimize design parameters of enclosures and structural parts to establish and deliver project performance objectives - Design and execute of tests using statistical tools to validate analytical models, identify risks and assess design margins - Create and present analytical and experimental results - Develop and apply design guidelines based on project learnings 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?

Amazon's Selling Partner Support handles tens of millions of contacts annually worldwide. The Titans Science team is transforming this experience by building AI agents that autonomously resolve seller issues, learn from every interaction, and continuously improve with minimal human intervention. These agents reason, remember, and adapt — from understanding the seller's context and selecting the right solution, to routing contacts optimally, automating resolution end-to-end, and augmenting associates with AI when human judgment is needed. We do this in deep partnership with multiple engineering and product partners. We are looking for an Applied Scientist who wants to work at the intersection of agentic architectures and large-scale production systems. You will be directly connected to the problems sellers face every day, translating real customer pain into science solutions that operate at massive scale. You will frame ambiguous business challenges as tractable ML problems to shipping systems that measurably improve millions of seller interactions. Key job responsibilities - Own end-to-end research and development of RL-based agent improvement systems — from problem formulation through production deployment and impact measurement. - Design novel approaches to preference learning, reward modeling, and policy optimization in the context of conversational agents operating over real-world tools and APIs. - Build and maintain evaluation frameworks that measure agent quality across multiple dimensions: helpfulness, correctness, safety, and alignment with operational standards. - Collaborate with a team of scientists that work on forefront of Natural Language Understanding, Optimization, Machine Learning and Statistics - Partner with 10+ engineering teams to deploy models into production systems serving sellers worldwide. - Publish research at top venues (NeurIPS, ICML, EMNLP, AMLC) — the complexity of our problems produces publishable work, and we actively support it. A day in the life You read the latest research papers and implement novel techniques by building rapid prototypes using AI-assisted coding tools, then taking what works from prototype to production. You collaborate closely product managers and engineering teams to translate seller pain points into deployed science solutions. You work closely under mentorship of senior scientists to accelerate delivery of agentic solutions from development to evaluation. Finally, you attend meetings with other Amazonians to stay connected to the seller experience by understanding the real problems sellers face so your models solve what actually matters. About the team Titans Science is a growing team of scientists building the AI that powers Amazon's seller support experience. We operate in across capabilities such as Agentic Systems, Knowledge Retrieval & Query Understanding, and Content Intelligence & Automation, each owning distinct problem spaces but sharing evaluation infrastructure and research insights. We work backwards from business problems, deeply understanding the problem space and domain, defining gold-standard datasets, success metrics, and guardrails. This lets us run parallel experiments, compare approaches rigorously, and ship the best Science models to production. We publish at internal conferences and external venues, and we actively invest in research that compounds over multiple product cycles. The team sits in Seattle/Vancouver and operates with high autonomy. Scientists own their domains end-to-end, from problem framing through production deployment. We value speed over perfection, scientific rigor over polish, and experimentation over debate. We value diverse experiences. Even if you do not meet all of the preferred qualifications listed above, we encourage you to apply. The team fosters an inclusive learning culture where individual growth is a priority — you will find mentorship, knowledge-sharing, and career-advancing resources here.

Amazon Devices is an inventive research and development company that designs and engineers high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health & Wellness, Amazon Echo, and Astro products. This is an exciting opportunity to bring generative AI to Amazon's consumer products, both on-device at the edge and in the cloud. Our compression platform delivers 20x to 100x neural network compression, but using it well still takes weeks of hands-on learning and expert intuition. The Edge AI Model Studio team exists to change that. We become the expert users so partner teams don't have to: we turn compression science into reliable, production workflows, and we package the results into a library of compression-ready student architectures that partners can run on their own. Our north star is simple. Training-to-deployment should feel like pushing a button, not a month-long science project. We are looking for an Applied Scientist to join Model Studio and help compress the next generation of models for edge and cloud deployment across modalities, including large language models, vision-language models, speech and audio models, and omni models that reason jointly over text, audio, and video. You will apply and extend state-of-the-art compression recipes to real models, define the benchmarks and evaluation methodology that make trade-offs explicit, and build the reference implementations that let other teams deploy compressed models without our help. You will work backwards from deployment constraints such as memory, latency, throughput, power, and cost, which differ across edge and cloud targets, partnering closely with fellow scientists, platform and compiler engineers, hardware architects, and product teams. The role sits on two frontiers at once. Compressing a model effectively and healing it back to quality means staying current not just with the latest compression techniques, but with the rapidly evolving model architectures themselves, and understanding deeply how each one works inside. You will take ownership of project-level delivery, apply advanced compression across a wide range of real models, and have room to grow your scope and technical influence. Key job responsibilities - Apply and extend compression recipes (knowledge distillation, structured pruning, and post-training and quantization-aware quantization including low-bit and mixed-precision) to assigned models, achieving 20x to 100x compression while preserving model quality. - Design and run healing recipes (fine-tuning and distillation that recover accuracy lost to compression), iterating on data mixes, objectives, and training settings until the compressed model meets its quality bar. - Track emerging model architectures and dissect how they work internally, so you can choose where to compress, anticipate where accuracy will break, and design recovery strategies grounded in the model's actual structure. - Build a library of compression-ready model entries: reference implementations, compression recipes, model cards, and benchmark results that partner teams can run self-service to produce deployment-ready artifacts for edge and cloud targets. - Define the datasets, benchmarks, and KPIs that matter for your models, and build evaluation methodology that makes accuracy, latency, memory, and cost trade-offs explicit. - Run fast feasibility gates on new model families and modalities before committing to long efforts, and pivot early when a candidate does not clear the bar. - Capture platform friction as high-signal feedback: minimal reproductions and tracked fix requests that help platform and compression-science partners root-cause issues, so partner teams never rediscover the same blockers. - Write reproducible, testable, well-documented code that meets the SDE I bar, so your recipes and results can be reproduced and built on by others. - Collaborate with Applied Scientists, platform and compiler engineers, hardware architects, and partner teams; mentor interns and help newer teammates ramp up. - Where appropriate and not precluded by business considerations, publish and present on Amazon's behalf at top ML venues such as NeurIPS, ICLR, and MLSys. A day in the life You pick up a vision-language model whose vision tower needs to fit tight memory, latency, and cost budgets for deployment. You configure a quantization-aware training run at the team's target compression ratio, then check the compressed checkpoint against a visual reasoning benchmark and find it recovers only part of the baseline accuracy. You design a healing run to close the gap, tuning the data mix and training objective to fine-tune the compressed model back toward the teacher's quality. The next checkpoint clears most of the gap but still lands short, so rather than assume the recipe is at fault, you dig into the evaluation harness and discover a benchmark filter is misaligned, deflating the score. You fix the filter, re-run, and confirm the healed model lands where the science predicts. You then package the work as a reusable model entry (recipe, model card, benchmark numbers, and a reference implementation a partner team can run on their own) and file a minimal reproduction of the harness bug so no one rediscovers it. A typical week mixes hands-on compression and evaluation with design discussions alongside fellow scientists and platform engineers. You run a fast feasibility gate on a new model family before committing to a long effort, profile a compressed model to confirm a real throughput gain, and turn a recurring friction point into a reusable pattern. You work in a small, fast-moving team where every recipe you harden compounds across future models and every partner you unblock ships faster. About the team We compress frontier models 20x to 100x and put them in the hands of millions of customers, everywhere from your pocket to the cloud: the device in your hand, the Echo on your counter, and the services behind them. The models the industry shipped last month, we are shrinking this month, across language, vision, speech, and omni. That is the job: take the best models in the world and make them small enough, fast enough, and cheap enough to run everywhere, without giving up the intelligence that makes them worth running. Edge AI Model Studio is the team that makes it real. We are the expert users of a compression platform that most of Amazon cannot yet wield, and our mission is to change that, turning weeks of expert intuition into recipes anyone can run. We are small, we move fast, and we own our work end to end: a result counts only when it ships with a recipe, benchmarks, and an artifact a partner team can run without us. Every recipe we crack compounds across every model that follows. If you want your science in real products at real scale, and you want to put the frontier of generative AI in the hands of millions of customers, come build it with us.

As part of the AWS Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. We are looking for a senior audio applied scientist with experience and expertise in speech and audio signal processing, machine learning, automatic speech recognition, and/or natural language processing to work on state-of-the-art solutions for applications including speech enhancement, voice analytics, and real-time transcription of conversational audio. The ideal candidate has collaborated closely with engineering teams to turn business problems and high level ideas into successful feature launches. Amazon Connect is a highly disruptive cloud-based contact center that enables businesses to deliver engaging, dynamic, and personal customer service experiences. Amazon Connect is the result of the ten years of development that went into building the tools Amazon uses to provide its award winning customer service at massive and launching it as a publicly available service. With Amazon Connect, you can create your own cloud-based contact center and be taking calls in minutes. Our team’s charter as part of the Amazon Connect organization is to think big, re-imagine, innovate, and deliver novel, state-of-the-art solutions to audio and video problems. We are interested in all aspects of audio, video, and media technology, and we leverage the latest machine learning and signal processing techniques to surprise and delight our customers. Our applications include real-time audio/video communications, audio/video scene analysis, anomaly detection, audio/speech/music/image/video processing, enhancement, analysis, synthesis and coding. We have the nimbleness of a small startup but, at the same time, the immense resources of AWS - the world leader in cloud computing - behind us as well. If you want to innovate on the state of the art technology while having a profound and direct impact on the end customer experience, this is the team to be on! About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred 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. 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 in the cloud.

The Devices and Services org at Amazon has been the innovation engine of consumer electronics at Amazon with the industry-leading Kindle e-readers, Fire tablets, Fire TV, Echo, the most popular smart speaker and Alexa, the leading AI assistant. We are looking for Senior Applied Scientist- Audio to join the Edge Technology team. We are responsible for all of the Echo audio features including Spatial audio and Home Theater. Your work will have a large impact on the lives of Echo customers as music listening is the most popular feature. Key job responsibilities In this role, you will: -Be the champion of Echo music processing technology innovation from ideation, proof of concept to productization -Propose new research projects, get buy-in from stakeholders, and lead the team for successful execution -Work closely with an inter-disciplinary product development team including outside partners to bring the prototype algorithm into commercialization -Be a team leader in an open and collaborative environment

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production Partner closely with other scientists to deliver large, multi-faceted technical projects Share and publish works with the broader scientific community through meetings and conferences Communicate clearly to both technical and non-technical audiences Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

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 - 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. - Mentor and develop fellow scientists by providing technical guidance, reviewing research, and fostering a culture of scientific rigor across the team. A day in the life You might start the morning sketching a methodology for measuring time-varying average treatment effects, then prototyping it in Python on real supply chain experiment data. You'll publish at top venues like NeurIPS, ICML, and JASA, and influence how scientists across SCOT think about causal inference in various settings. The problems are open, the data is unmatched in scale, and the methods you build will move billions of dollars in inventory decisions. If you want to do foundational research that makes real impact across Amazon's supply chain, this is where you do it. At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! 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.