Careers

Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning 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 Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning 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 Automated Reasoning, 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 automated reasoning 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.

Are you a scientist interested in pushing the state of the art in Information Retrieval, NLP, Large Language Models and fine-tuning LLMs? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? Answer yes to any of these questions, and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, generative AI, large-scale data systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personalized and up-to-date recommendations that are related to their interests. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future. Key job responsibilities As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations, information retrieval and large language models and fine-tuning models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Please visit https://www.amazon.science for more information. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute 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 organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features. You will mentor junior members and help them grow. About the team The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.

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!

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Key job responsibilities As the Sr. Manager, Applied Science in the Prime Video Personalization and Discovery organization, you will be responsible for optimizing the complete customer experience, across the touch points throughout customers’ discovery journey. This includes building AI and optimization solutions, working with the business, product, engineering teams to deliver the optimal balance of customer delight and business outcomes. Responsibilities include direct management of senior engineers and scientists, setting vision and long-range technical strategy, product definition, roadmap planning, driving cross-functional execution, developing and maintaining experimentation and production services, owning ML and engineering excellence quality bar, and customer and stakeholder communication. Additionally, as our organization is growing, hiring top-notch engineers and scientists will be a key focus. About the team Prime Video Personalization and Discovery (PVPD) is dedicated to creating a highly personalized content discovery experience that not only delights our customers but also drives both short-and long-term business goals. Our scope includes personalized recommendations, search, marketing, and the advanced machine learning technology and infrastructure that underpins these experiences. Our mission is to automate and enhance customer engagement through personalization, using ML and Generative AI. To drive these efforts, Prime Video is seeking a visionary science leader to spearhead our investments in machine learning (ML) and artificial intelligence (AI) to reimagine the next-generation search experience on Prime Video. You will oversee a large team delivering against our ML strategy, overseeing the design of our ML stack, and ensuring the quality of our models. Your success in this role will depend on your deep expertise in search, personalization, discovery, AI/ML, Generative AI, and your passion for entertainment. This is a unique opportunity to influence the future of television for billions of viewers worldwide. As a center of excellence in machine learning, we are committed to leading the way in adopting and advancing cutting-edge technologies. We publish our research internally and externally, and this role will place you at the forefront of applying Generative AI at scale, using Amazon’s rich datasets. You will have a direct impact on shaping the future of entertainment, driving massive customer experience improvements, and achieving critical business KPIs.

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.

Amazon brings buyers and sellers together. Our retail customers depend on us to give them access to every product at the best possible price. Our sellers depend on us to give them a platform to launch their business into every home and marketplace. Making this happen is the mission of every scientist in North America Stores (NAS) organization. To this end, the Science team is tasked with: · Building and deploying AI / ML models and LLM-powered systems that lead to hundreds of millions in business impact across supply chain optimization, customer engagement, and cultural relevance at Amazon scale · Partnering with product teams in evaluating the financial and operational impact of new product offerings. · Partnering with science teams across other organizations to develop state of the art algorithms and models. · Carrying out independent data-backed initiatives that can be leveraged later on in the fields of network organization and financial modeling of processes. · Publishing papers in both internal and external conferences / journals. In order to execute the above mandate we are on the look out for smart and qualified Applied Scientists who will own projects in partnership with product and research teams as well as operate autonomously on independent initiatives that are expected to unlock benefits in the future. Our team builds science-backed systems that directly influence vendor negotiations, forecasting, buying, product discovery for secondary language customers, and inventory management for North America's retail business. Key job responsibilities As an Applied Scientist, you are able to use a range of artificial intelligence and operations research methodologies to solve challenging business problems when the solution is unclear. Key responsibilities include: Develop workflows that combine ML models with optimization engines, similarity search, and human-in-the-loop capabilities to automate complex business processes Build scalable data and inference pipelines using AWS services (SageMaker, Bedrock, FAISS, Andes) to process 100M+ ASINs and serve real-time predictions in production Design and execute rigorous experimentation frameworks including weblabs, IPC labs, and causal inference methods to validate model impact and drive launch decisions Collaborate cross-functionally with engineering, product, and business teams to translate ambiguous business problems into well-scoped science solutions with clear success metrics

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.

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.