Careers

The Amazon Search team creates customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. The Search Relevance team focuses on several technical areas for improving search quality. In this role, you will invent universally applicable signals and algorithms for training machine-learned ranking models. The relevance improvements you make will help millions of customers discover the products they want from a catalog containing millions of products. You will work on problems such as predicting the popularity of new products, developing new ranking features and algorithms that capture unique characteristics, and analyzing the differences in behavior of different categories of customers. The work will span the whole development pipeline, including data analysis, prototyping, A/B testing, and creating production-level components. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world’s leading Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Please visit https://www.amazon.science for more information Key job responsibilities Your responsibilities include but not limited to: * Analyze the data and metrics resulting from traffic into Amazon's product search service. * Design, build, and deploy effective and innovative ML solutions to improve search ranking. * Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production. * Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IR.

Trusted by more startups around the world, AWS makes the power of cloud computing accessible for all by giving founders everywhere access to the same technology that powers the world's largest companies. With nearly two decades of experience supporting hundreds of thousands of startups, including 80% of unicorns, we democratize cloud computing to help founders bring their innovative ideas to life. We support founders at every stage of their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Data is central to how we do this: it helps us identify high-potential startups early, personalize the guidance we deliver, and prioritize where we can create the most value for founders and for AWS. We are seeking an Applied Science Manager to lead a team of applied scientists and analysts building the data and machine learning capabilities behind AWS Startups. You will own the science roadmap end-to-end, from the data foundation that unifies signals about founders, startups, and their products, through a portfolio of machine learning models, to the surfaces that put insights in the hands of the teams and products that serve startups. You will balance hands-on technical leadership with people management, setting the technical bar for your team while developing their careers. Key job responsibilities · Lead, coach, and grow a team of applied scientists, business intelligence engineers, and business analysts; hire and develop talent and set a high technical bar. · Own and prioritize the team's science roadmap across data foundation, model development, and the delivery of insights into products and internal tools. · Set technical direction for the team's machine learning models and data assets, balancing rapid experimentation with production quality, cost, and reliability. · Scope scientific projects, design and evaluate experiments, and ensure models are productionized and deliver measurable business impact. · Establish measurement, evaluation, and operational-excellence standards so model quality and impact are quantified and defensible. · Partner with product, engineering, design, and go-to-market teams to translate science into products and repeatable, scalable outcomes. · Communicate strategy, results, and trade-offs clearly to technical and non-technical leaders through written narratives and business reviews. · Foster a culture of scientific rigor, rapid experimentation, and operational excellence, and proactively identify and escalate risks with clear mitigation plans. About the team The AWS Startups team builds innovative products and platforms that support startup customers throughout their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Our portfolio serves hundreds of thousands of startup customers globally, and we partner with business development, field marketing, and solutions architecture teams worldwide. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder.

The Amazon Smart Vehicles (ASV) science team is seeking a passionate and skilled Applied Scientist with extensive expertise in advanced LLM technologies. This role involves innovating in rapidly evolving areas of AI research, focusing on creating personalized services to enhance drivers' and passengers' experiences. Your work will aim to simplify their lives, keep them informed, entertained, productive, and safe on the road, with direct application to prominent Amazon products. If you have extensive expertise in LLMs, natural language processing, and machine learning, along with experience in high-performing research teams, this could be the perfect opportunity for you. Our dynamic and fast-paced environment demands a high level of independence in decision-making and the ability to drive ambitious research initiatives through to production. You will collaborate closely with other science and engineering teams, as well as business stakeholders, to ensure your contributions are both impactful and delivered with maximum efficiency. Key job responsibilities - Leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI) - Work with talented peers to lead the development of novel algorithms and modeling techniques to advance the state of the art with LLMs - Collaborate with other science and engineering teams as well as business stakeholders to maximize the velocity and impact of your contributions About the team This is an exciting moment to lead in AI research and application. As part of the Amazon Smart Vehicles science team, you have the opportunity to shape the future by enhancing information-driven experiences for Amazon customers around the globe. Your work will directly influence customers through innovative products and services powered by language and multimodal technology!

Embark on an exciting science journey with International Emerging Stores Payments as we apply LLM techniques to improve the customer experience across multiple countries where we operate in. You will impact 1/4 of the world's population. Key job responsibilities 1. Translate ambiguous opportunities in to defined science problem statements 2. Evaluate and identify the right ML algorithm to solve for the problems. 3. Identify and build the right features for the Models 4. Partner with IML team in applying GenAI development strategies to accelerate model development 5. Publish papers to internal and external conferences.

We are looking for a Senior Applied Scientist to help drive the research and development of real-time multimodal conversational AI. You will contribute across two focus areas: advancing foundation models for speech and audio, and building the post-training systems (reward modeling, reinforcement learning) that shape natural, human-like conversational behavior. You will own a significant research area and contribute across the full model lifecycle — from pre-training and architecture design through post-training alignment and real-time deployment. You will work at the frontier of what’s possible in conversational AI, with the compute, data, and runway to pursue problems that few teams in the world have the resources to tackle. As a Senior Scientist, you will drive the technical execution of your research area, contribute to the team’s roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment. Key job responsibilities What You’ll Do Foundation Model Scaling - Help build and train large-scale multimodal foundation models for real-time speech and audio generation, from architecture design through production-scale training - Advance the scaling and efficiency of conversational models, including the relationship between data, model size, and real-time performance - Design model architectures informed by hardware constraints and inference requirements, working with inference engineers to ensure models are servable from inception - Develop training methodologies for multimodal models that jointly process and generate speech, language, and audio in real-time streaming contexts - Contribute to the state of the art on efficient architectures and training methods for conversational AI at scale Post-Training & Reinforcement Learning - Design and build reward models and reward functions for speech systems — capturing naturalness, fluency, conversational quality, and real-time responsiveness - Develop and apply reinforcement learning methods to shape conversational behavior — teaching models natural timing, responsiveness, and fluid interaction - Build parts of the post-training pipeline from SFT through RL alignment, optimized for real-time multimodal outputs rather than text-only generation - Design evaluation frameworks that capture the quality dimensions unique to real-time conversation (latency sensitivity, audio quality, prosody, interaction naturalness) Real-Time Perception & Generation - Advance the team’s capabilities in real-time perception — the ability of the model to process incoming audio/speech while simultaneously generating responses - Develop techniques for natural interactive systems where the model handles concurrent input and output with human-like timing - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As an Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate 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 Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.

The Shopping Convo Foundations Team - Pre-purchases Science is looking for an Senior Applied Scientist with expertise in Artificial Intelligence and Machine Learning to drive scientific innovation that expands Amazon's product catalogue. Our goal is to leverage AI/ML solutions to enhance catalogue coverage with high precision. In this role, you will lead the research and development of novel machine learning approaches to solve complex catalogue expansion and product attribute challenges. You lead the design and develop state-of-the-art ML models, conduct rigorous experimentation, translate scientific breakthroughs into production-ready solutions, and guide a set of junior scientists. You will work closely with ML Engineers and Software Development Engineers to optimize model performance, ensure scalability, and deploy low-latency solutions at Amazon scale. About the team Our team is a horizontal applied science group that works across the full lifecycle of brand and product data extraction and quality. We span seven workstreams — from products sourcing and Brand entitlement, to designing and evaluating extraction strategies for ASINs, offers, and brand attributes at scale, as well as relevance modeling and search. We drive root cause analysis through human-in-the-loop evaluation, improve how catalog data surfaces in search, optimize business metrics tied to data quality, and build brand intelligence capabilities. This cross-cutting scope positions the team as a connective layer across product, engineering, and science — ensuring that improvements in one area compound across the system rather than remain siloed.

The Foundational AI (FAI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities As an Applied Scientist with the FAI team, you will support the development of RL Gyms, assess their usefulness for the frontier model advancement and build techniques to advance the state of the art with LLMs. You will support the foundational model development in an applied research role. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The FAI team has a mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

Amazon WW Global Selling is looking for a dynamic, highly motivated Sr Data Scientist to join the central PMO team. This is an unique opportunity to: Play a highly visible role in an exciting and fast paced business Drive high impact products and initiatives to continuously improve Sellers' experience, growth and profitability Innovate with advanced seller insights and influence cross-functional and global partners for great ideas and products. Influence and drive decisions of senior leadership This role is well-suited for someone with a strong economics or causal ML foundation who wants to apply rigorous statistical thinking to real product decisions at scale. You'll need to be comfortable writing SQL, working with imperfect data, and partnering with stakeholders to turn analysis into product impact. The ideal candidate will be strong at performing deep dives to derive insights on Seller behaviors, identify key pain points and needs from Seller perspective, and keen on AI-powered innovations, including: 1. Science-backed segmentation with a self-iterating feedback loop: Clustering algorithms (k-means, hierarchical, DBSCAN), Feature engineering from catalog and sales data, Feedback-loop model design, Ability to explain model logic to non-technical stakeholders 2. Econometric model to identify high expansion intent sellers: Propensity scoring and intent modeling (logistic regression, gradient boosting, neural), Behavioral sequence modeling, Feature engineering from sparse event-driven data, Classification threshold calibration 3. Post-Enrollment Outcome Models: Causal inference and treatment effect estimation (DiD, PSM, synthetic control, IV), A/B test design, Reinforcement learning Science-backed graduation signals framework: Time series modeling and threshold detection, Survival analysis or time-to-event modeling, Bayesian updating for threshold calibration, Real-time or near-real-time scoring pipeline design 4. Familiarity with GenAI and LLM frameworks; experience embedding science models into AI-powered products or agents Key job responsibilities Key job responsibilities - Use advanced statistical and machine learning techniques to extract insights from complex, large-scale data sets - Partner with business/product stakeholders and senior science peers to identify strategic data-driven opportunities to improve the seller experience with a focus on seller economics domain - Communicate findings, conclusions, and recommendations to technical and non-technical stakeholders - Stay up-to-date on the latest data science tools, techniques, and best practices and help evangelize them across the organization - Design and implement end-to-end data science workflows, from data acquisition and cleaning to model development, testing, and deployment - Support scalable, self-service data analyses by building datasets for analytics, reporting and ML use cases About the team We are WW Global Selling PMO team. We collaborate with WWGS business teams to identify key Seller needs and partner with global product teams to develop tools and solutions to improve Seller experience and sustainable growth.

Amazon WW Global Selling is looking for a dynamic, highly motivated Sr Data Scientist to join the Selection & Pricing Intelligence team. This is a unique opportunity to: - Play a highly visible role in an exciting and fast-paced business - Drive high-impact frameworks and initiatives that shape which selection Amazon prioritizes globally and how competitively it's priced - Innovate with advanced demand and pricing models, influencing cross-functional and global partners on selection strategy and competitive positioning - Influence and drive decisions of senior leadership, with your models feeding directly into VP-level reviews This role is well-suited for someone with a strong statistics, causal ML, or economics foundation who wants to apply rigorous quantitative thinking to real selection and pricing decisions at scale. You'll need to be comfortable writing SQL, working with imperfect and fragmented cross-marketplace data, and partnering with domain strategists to turn analysis into business action. The ideal candidate will be strong at deriving insight from complex demand and pricing signals, testing whether frameworks generalize across marketplaces and channels, and keen on where AI can scale repeatable analytical judgment. Key job responsibilities - Use advanced statistical and machine learning techniques to extract insights from complex, large-scale, cross-marketplace data sets spanning selection demand and pricing competitiveness - Extend and validate demand models (Unmet Demand Model and related frameworks) to support selection opportunity sizing, including entitlement methodology and compliance-adjusted pool sizing - Build and own pricing measurement frameworks (e.g., price-competitiveness decomposition, competitive positioning metrics) with rigor suitable for senior leadership review - Partner with business/product stakeholders and senior domain strategists to identify strategic, data-driven opportunities across both the selection and pricing intelligence domains - Test whether existing frameworks generalize across marketplaces, channels, or seller cohorts — surfacing where a model breaks down before it drives a flawed business decision - Communicate findings, conclusions, and recommendations to technical and non-technical stakeholders - Design and implement end-to-end data science workflows, from data acquisition and cleaning to model development, testing, and deployment - Support scalable, self-service data analyses by building datasets for analytics, reporting, and ML use cases - Stay current on data science and AI tooling, and help identify which analytical workflows are strong candidates for automation