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Sr. Applied Scientist, Prime Video - Title Lifecycle Presentation
US, WA, Seattle
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. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! The Prime Video Title Lifecycle Presentation team sits at the intersection of science, experimentation, and customer experience. We leverage data signals and rigorous testing to present the most engaging information about our content to customers at precisely the right moment. Our mission is to ensure every customer interaction with Prime Video content is informed, relevant, and compelling in order to drive discovery and engagement across our vast catalog. We're seeking a Sr. Applied Scientist who excels at building sophisticated machine learning systems for content presentation and discovery. The ideal candidate brings deep expertise in: - Multi-modal embeddings for rich metadata representation, enabling nuanced understanding of content attributes and customer preferences - Contextualized ranking systems that adapt to customer intent, viewing context, and real-time signals - Reinforcement learning frameworks that create continuous improvement loops, allowing our systems to learn and optimize from customer interactions over time - General modeling techniques with strong fundamentals in machine learning and statistical methods - Recommender systems experience, with proven ability to build and scale personalization solutions You'll work with technology to solve complex problems in content discovery, leveraging large-scale data to create experiences that delight millions of Prime Video customers worldwide. Key job responsibilities - Lead Cross-Functional Science Initiatives: Drive a diverse portfolio of applied science projects spanning recommender systems, generative AI agent development and evaluation across multiple modalities, and computer vision applications. Demonstrate both breadth of understanding across technical domains and sufficient depth in each area to effectively lead multiple concurrent initiatives to successful outcomes. - Bridge Science and Engineering for Production-Scale Deployment: Partner with engineering teams to productionize machine learning models at Prime Video scale. Develop production-ready science code that meets engineering standards for performance, reliability, and maintainability, ensuring seamless transition from research to deployment. - Mentor and Develop Technical Talent: Provide technical mentorship and guidance to junior scientists and engineers on applied science methodologies, best practices, and professional development. Foster a culture of scientific rigor and continuous learning within the team.
Applied Scientist, Fauna
US, NY, New York
We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers
Research Scientist, WW Sustainability
US, WA, Seattle
Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. We are looking for a Research Scientist to join our growing Sustainability team to drive the science behind value chain decarbonization. This role will establish Amazon's scientific methodologies for sector- and cross-sectoral decarbonization mechanisms, and establish benchmarks for automated validation and risk assessment. As a Research Scientist, you will be responsible for independently leading assessments of environmental issues across the full spectrum of Amazon businesses and evaluating sustainability impacts across the value chain. You will independently develop quality frameworks and methodologies that enable Amazon to scale procurement of high-quality environmental interventions while maintaining scientific rigor and environmental integrity. Key job responsibilities Develop quality assessment frameworks for complex environmental interventions, baseline-setting approaches, and measurement methodologies Build quantitative benchmark and statistical models that enable scalable evaluation across heterogeneous data sources Create attribution methodologies for supply chain interventions across Amazon's diverse footprint Develop social and environmental safeguard criteria that integrate community impact assessments Collaborate with cross-functional teams including procurement, sustainability operations, and business units to translate scientific methodologies into operational requirements Work under the direction of senior business leaders while acting as lead Subject Matter Expert for value chain decarbonization science, including designing and leading research, data collection, modeling, documentation, interpretation, and validation About the team Diverse Experiences: World Wide Sustainability 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. Inclusive Team Culture: It’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (inclusive diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & 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, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Applied Scientist II, Alexa Ads
IN, KA, Bengaluru
Alexa+ is the world’s best Generative AI powered personal assistant / agent for consumers, and is becoming the conversational AI interface for Amazon services with the launch of Alexa for Shopping on Amazon.com and Amazon mobile app. At Alexa Ads, we are creating industry's first and most advanced Agentic Advertising products to drive Agentic Commerce. We are seeking an Applied Scientist to join our newly expanding team in India focused on Alexa Agentic/Conversational Ads and Personalization. In this role, you will build machine learning models that seamlessly and naturally integrate relevant advertising into the Alexa experience while deeply personalizing user interactions. You will work closely with other scientists, engineers, and product managers to take models from conception to production. Key job responsibilities - Design, develop, and evaluate innovative machine learning and deep learning models for natural language processing (NLP), recommendation systems, and personalization. - Conduct hands-on data analysis and build scalable ML pipelines. - Design and run A/B experiments to measure the impact of new models on customer experience and ad performance. - Collaborate with software development engineers to deploy models into high-scale, real-time production environments. About the team We are building a new science team in Bangalore to solve some of the most impactful problems in computational advertising. This isn't about tweaking existing models as we are rethinking how ads are ranked, priced, and personalized across voice-first and screen-first surfaces. These are problems that don't have textbook solutions. Key points to note about the team: 🧪 Greenfield team - you are not joining a mature org with rigid processes. You will shape the science roadmap, pick the problems, and define the culture from day one. 📈 Direct business impact — your models directly drive revenue. No yearly cycles to see if your work matters. 🌏 Global scope, local autonomy — collaborate with scientists and engineers across Seattle, Sunnyvale, and Bangalore, but own your problem space end-to-end. 🎓 Ship AND Publish: We encourage top-tier publications (NeurIPS, ACL, EMNLP, KDD, ICML, WWW) while ensuring your research hits production.
Applied Scientist I
IN, TS, Hyderabad
Are you passionate about solving complex problems with machine learning and scientific rigor? As an Applied Scientist I at Amazon, you will translate real-world business challenges into well-defined scientific problems and build solutions that directly benefit customers. You will work alongside experienced scientists and engineers, applying your expertise in areas such as natural language processing, computer vision, or robotics to design experiments, develop models, and deliver production-ready code. This is a role where your curiosity and technical depth will drive meaningful impact from day one. Key job responsibilities - Design, develop, and implement machine learning models and algorithms to solve well-defined business problems, mapping business goals and metrics to scientific approaches and evaluation criteria. - Write secure, stable, testable, and maintainable production code, applying state-of-the-art data structures and algorithms while following software development best practices at a high quality bar. - Conduct rigorous experiments to evaluate model performance, benchmark results against current research, and iterate on solutions to improve accuracy and customer outcomes. - Collaborate with team members to scope technical approaches, communicate findings through internal research reports, and contribute to peer-reviewed publications when aligned with business needs. - Stay current with research trends in your area of expertise, champion the adoption of recent scientific advancements, and help onboard and mentor scientist interns. A day in the life You might start your morning reviewing experiment results from a model you trained, analyzing performance metrics and identifying areas for improvement. After a design discussion with your team, you refine your approach and push updated code for review. In the afternoon, you read a recent research paper recommended by a senior scientist, exploring whether a new technique could improve your current solution. You wrap up by documenting your methodology so teammates can understand and build on your work. About the team Our team is focused on applying scientific methods and machine learning to solve problems that matter to Amazon's customers. We value rigorous experimentation, clear communication, and a collaborative environment where scientists at every stage of their career can grow. We are building toward solutions that push the boundaries of what is possible, and we are looking for curious, thoughtful scientists who want to contribute to that mission and learn alongside a supportive group of peers.
Applied Scientist I, Worldwide Returns & Recommerce - Science
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team! Key job responsibilities * Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. * Use SQL to query and analyze the data. * Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. * Use machine learning and analytical techniques to create scalable solutions for business problems. * Research and implement novel machine learning and statistical approaches. * Mentor interns. * Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. About the team When a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models: XGBoost, BERT, Vision Transformers, Large Language Models.
Applied Scientist, PXT Central Science
US, WA, Seattle
The People eXperience Technology (PXT) Central Science (PXTCS)'s mission is to make PXT the most scientific, technologically proficient, and inclusive HR organization in the world. PXTCS does this by accelerating scientific rigor in business-led initiatives, working backwards from employee experience, and delivering science-driven products that improve the well-being of and value of work for Amazonians worldwide. We are seeking an Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning predictive modeling, computer vision, natural language processing, recommendation systems, and generative AI applications. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions. Key job responsibilities - Apply and adapt state-of-the-art scientific techniques to solve well-defined problems in employee experience, using reasonable assumptions, data, and customer requirements. - Design, develop, and implement small-to-medium ML components with input and guidance from senior scientists, taking ownership of the code in your components. - Write secure, stable, testable, maintainable, well-reviewed code (at the SDE I bar) to deliver solutions into production that benefit customers and the business. - Collaborate with cross-functional partners to understand business context and impact, and help mentor interns. - Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership About the team The People eXperience and Technology Central Science Team (PXTCS) uses economics, applied science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.
Sr Applied Scientist, WW Prime , WW Prime - AI/ML Science
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems. Key job responsibilities Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
Applied Scientist, International Seller Services
US, WA, Seattle
Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, emerging technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. A day in the life Push the boundaries of applied science - fine-tune large language models and develop novel NLP techniques to crack complex challenges in seller acquisition, content generation, and catalog understanding Work with data at massive scale - tap into some of the richest e-commerce datasets in the world to uncover patterns, generate insights, and drive real business impact Turn research into reality - prototype bold ideas, then partner with engineers to bring your models into production, balancing scientific rigor with real-world scalability Think globally, deliver worldwide - collaborate with leaders and teams across Europe, Japan, China, and Australia to build solutions that generalize across international marketplaces Solve problems that matter - translate ambiguous business challenges into well-scoped science problems that directly serve sellers and customers around the globe Collaborate with the best - engage in design reviews, contribute to technical thought leadership, and learn from a diverse community of world-class scientists and engineers Never stop learning - stay at the frontier of NLP, LLMs, and applied ML, bringing the latest research advances into your work Own your impact - operate with a customer-obsessed mindset where every model you build helps sellers thrive and expands selection for customers worldwide
Sr. Communication Systems Research Scientist – Radio Resource Management
US, TX, Austin
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites providing low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communication Systems Research Scientist, this role owns the research and system design of the radio resource management (RRM) and radio access layers of Amazon Leo’s direct-to-device (D2D) system, delivering 3GPP-compliant service to unmodified commercial handsets. The Role: Be part of the team defining the communication system and architecture of Amazon’s direct-to-device wireless network and analyzing its system level performance: beam and cell capacity, spectral efficiency, coverage, latency and service availability. This is a unique opportunity to innovate with few legacy constraints, in a segment where the standard itself is still being written. This role leads the research and system design of radio resource management (RRM) for a 3GPP Non-Terrestrial Network (NTN), where D2D upends terrestrial assumptions: a power-limited handset with a near-isotropic antenna, very large cells, hopping beams, large time-varying delay and Doppler, and scarce shared spectrum. RRM in time, frequency and spatial domains is the focus, but the role reasons across the stack, from L1/L2 up through RRC, NAS and 5GC interworking. Agentic AI is expected to be a standard part of the work for development, optimization, tests and debugging, with the scientist accountable for the algorithms, models and conclusions. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities • Research, design and specify RRM algorithms for Amazon Leo’s 3GPP-based D2D system: MAC scheduling, link adaptation, power control, HARQ strategy, DRX, admission and congestion control, and load balancing, mapping 5QI and QoS flow requirements to scheduler behavior across voice, messaging, emergency and data services. • Treat beam management as part of joint resource optimization, not a standalone process, optimizing it with band assignment, packet scheduling and user pairing in multi-user MIMO (MU-MIMO). • Define the RRM framework for NTN conditions: earth-fixed and earth-moving cells, large time-varying propagation delay, ephemeris-assisted timing and Doppler pre-compensation, extended timing advance, selective HARQ feedback disabling, feeder link and satellite handovers, and interference and spectrum sharing across beams, satellites and terrestrial networks using the same MNO spectrum. • Design mobility and service continuity for a network where the base stations (i.e., satellites) move rather than the user: idle and connected mode mobility, location and time based conditional handover, cell reselection, paging, tracking area design, and NTN-to-terrestrial continuity. • Specify supporting L1/L2 elements with the PHY team: numerology under Doppler, PRACH and initial access, coverage enhancement through repetition, synchronization at low SNR, receiver abstraction, and FEC and BLER modeling for link adaptation. • Keep the radio design coherent with the networking layers: RRC and NAS, RLC and PDCP over long-RTT links, CU/DU split, NTN gateway and 5GC/EPC integration, and transport behavior. • Develop link-level and system-level simulators capturing constellation dynamics, beam patterns, handset characteristics, traffic models and RRM behavior, and use agentic AI across that loop: build and refactor simulation code, scale parameter sweeps, optimize scheduler and link adaptation parameters, explore configuration spaces too large to sweep by hand, maintain regression tests, and triage failures across logs, traces and over-the-air captures. • Translate research into system requirements and implementation-level specifications, and work with modem, payload, ground, RF, ASIC and Testbed teams through integration, field trials and link bring-up, root-causing gaps between simulation, implementation and over-the-air behavior in a fast-paced environment. • Represent Amazon Leo in 3GPP and other standards development organizations, develop and defend contributions on NTN and D2D work items, and contribute patents and publications.
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