Careers

The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. As a Data Scientist on our team, you'll work with complex data, develop statistical methodologies, and provide critical product insights that shape how we build and optimize our solutions. You will work closely with your Analytics and Applied Science teammates. You will build frameworks and mechanisms to scale data solutions across our organization. If you are passionate about redefining how AI can improves everyone's daily life, we’d love to hear from you. Key job responsibilities Key job responsibilities Problem-Solving - Analyze complex data to identify patterns, inform product decisions, and understand root causes of anomalies. - Develop analysis and modeling approaches to drive product and engineering actions to identify patterns, insights, and understand root causes of anomalies. Your solutions directly improve the customer experience. - Independently work with product partners to identify problems and opportunities. Apply a range of data science techniques and tools to solve these problems. Use data driven insights to inform product development. Work with cross-disciplinary teams to mechanize your solution into scalable and automated frameworks. Data Infrastructure - Build data pipelines, and identify novel data sources to leverage in analytical work - both from within Alexa and from cross Amazon - Acquire data by building the necessary SQL / ETL queries Communication - Excel at communicating complex ideas to technical and non-technical audiences. - Build relationships with stakeholders and counterparts. Work with stakeholders to translate causal insights into actionable recommendations - Force multiply the work of the team with data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets and product insights - Collaborate with cross-functional teams. Mentor teammates to foster a culture of continuous learning and development

About us As part of the AWS Applied AI Solutions organization, our vision is 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. Our team combines Amazon's real-world experience with state-of-art AI to create opinionated, turnkey solutions that are no-brainers to buy and easy to use. We're building applied AI solutions that businesses love and trust. Our ambition is to become the partner companies rely on to run their business every day—putting AI to work to deliver better customer experiences, operational excellence, and faster innovation. We're a fast-moving, scrappy team building a new agentic product from the ground up. If bias for action is your favorite leadership principle, you'll fit right in. The Role We're seeking a talented Senior Applied Scientist with expertise in large language models, agentic systems, and foundational models. You will be responsible for building the state-of-art multi-agent system, using a handful of methods including fine-tunning, reinforcement learning, etc. You'll accelerate our customer-facing features, contribute to our collaborative and innovative culture, and bring state-of-art applied research that raises the bar for the entire team. Key job responsibilities • Drive end-to-end GenAI projects with high complexity and ambiguity from conception to production • Build, optimize, and deploy ML models while collaborating with software engineers for productionization • Research innovative machine learning approaches and identify new opportunities for GenAI applications • Perform hands-on analysis and modeling of large datasets to develop actionable insights • Establish scalable, automated processes for data analysis, model development, and validation • Present results to senior leadership and collaborate with cross-functional teams About the team Diverse Experiences AWS 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. 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. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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. 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.

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 their 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 leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. We are hiring an Economist on the team to develop the next generation of incrementality measurement products, capturing the effect of advertising in driving sales as well as the effects of measurement tools on advertiser engagement with Amazon. As an Economist on the team, you will lead the design, implementation, and validation of large-scale causal inference methodologies to capture these properties. You will communicate your results with science and business leaders, and partner with other scientists and engineers to carry solutions into production. Key job responsibilities Leverage deep expertise in causal inference to develop robust, causally grounded ads measurement solutions Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents 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 and leaders Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process

The Central Learning Solutions (CLS) Science team in Amazon Operations builds state-of-the-art solutions for enhancing leadership and associate development across the organization. We develop technology and mechanisms for building personalized learning experiences based on the profiles of different learners and assess the post-training performance curves for different learner profiles. As a Data Scientist on the team, you will be driving the data science roadmap for the CLS Science team. You will leverage your knowledge in statistics and econometrics, estimate the causal impact of training interventions, recommend the right interventions for a given learner profile, and measure the post-launch success of these interventions through A/B weblabs. These insights will help in dynamically changing the training content of Learning & Development courses and unlock opportunities to improve both training effectiveness and learner experience. You will collaborate effectively with internal stakeholders and cross-functional teams for solving business problems, create operational efficiencies, and deliver successfully against high organizational standards. Key job responsibilities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation. - Use advanced causal inference methodologies to estimate the learning curves for different learner profiles and the effectiveness of training content. - Perform statistical analysis and statistical tests including hypothesis testing and A/B testing. - Implement new statistical, machine learning, or other mathematical methodologies to solve specific business problems. - Present deep dives and analysis to both technical and non-technical stakeholders, ensure clarity, and influence the strategy of business partners. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! 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 Central Learning Solutions (CLS) is the centralized technical team that builds Learning & Development products for North America Operations employees. CLS Science teams owns the science components that power the different products on Impact Estimation, Self Serve Analytics, Content Understanding, and Personal learning.

Amazon Search is reinventing how customers find products through natural-language and semantic understanding. We are looking for an Applied Scientist II to push the science behind Natural Language Search that interprets complex, constraint-rich shopping queries, retrieves and ranks the most relevant products. You will build and ship large-scale relevance and ranking models that measurably reduce the rate at which customers see irrelevant results, working on problems that span query understanding, semantic matching, and contextual ranking at Amazon scale. Key job responsibilities - Design, train, and ship deep-learning ranking and semantic-matching models that improve search relevance and reduce how often customers see irrelevant results, across hard query types. - Build the training data and evaluation methods that make these models work: synthetic and historical labels, hard-negative mining, and targeted sampling at the cases where search fails. - Develop signals that match product attributes to what the customer actually asked for. - Run offline and online A/B experiments, analyze precision/recall tradeoffs, and iterate to launch. - Work with engineers and scientists across teams to take models from prototype to production at Amazon scale. A day in the life You work alongside scientists and engineers on some of the hardest open problems in search relevance, teaching models to understand what customers really mean when they ask for something specific and nuanced. A typical day blends model development and data curation with sharp experiment analysis: diagnosing where search breaks down for a query segment, designing the fix, and proving the gains through offline metrics and live A/B tests that reach real Amazon customers. The work spans the full range, from surgical fixes that resolve stubborn failure pattern to broad modeling changes that move relevance for millions of queries at once. You'll see your ideas go from whiteboard to production fast, present results regularly to wider team, and help shape the team's relevance roadmap worldwide. About the team We are the science team behind Amazon's semantic search relevance and ranking. We own the models that understand nuanced, multi-constraint shopping queries and show products customers actually want. We operate close to production, measure ourselves on real customer-impact metrics, and run a culture of fast, rigorous experimentation. Every model decision is grounded in data.

We are Amazon's central Responsible AI team. Our mission is to advance the science and practice of Responsible AI (RAI) to enable Amazon's tens of thousands of builders to build and deploy AI solutions to the high standards that our customers and society expect. As a scientist on this team, you will: - understand in depth the technical and scientific issues related to RAI, including controllability, security, privacy, safety, veracity, robustness, fairness, explainability, transparency, and governance - help define the strategies, priorities and metrics for RAI - solve open problems in RAI to unblock traditional, generative and/or agentic AI use cases and solutions, publishing as appropriate - help develop our team - liase with internal and external stakeholders, including the academic community, on issues related to RAI About the team Diverse Experiences Amazon Security 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 Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security 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 Security, 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 security 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.

The Ads Trust Science team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.

We are seeking a Product Manager, Data Strategy & Physical AI to define and execute the long-term product vision for FAR's AI-powered robotics platform. The intersection of foundation models and physical intelligence is creating a once-in-a-generation opportunity to reimagine how intelligent systems perceive, reason, and act in the real world. We need a visionary product leader who can treat data as our primary competitive moat and translate research frontiers into scalable, production-grade capabilities. In this role, you will champion our core data strategy for foundation model creation, building a partner and tool ecosystem to systematically acquire, label, and iteratively improve physical AI datasets. You will architect a continuous data collection flywheel across deployed robot fleets, transforming real-world kinematics, video, and force-torque telemetry from edge operations back into high-fidelity training tokens. Recognizing the limitations of real-world environments, you will also lead the strategy to create high-fidelity synthesized datasets, utilizing advanced physics engines and simulation to generate diverse training tokens at massive scale. Key job responsibilities Data Acquisition & Labeling Ecosystem: Establish the partnerships, tools, and vendor pipelines necessary to acquire, curate, and continuously label multi-modal datasets for training large-scale models. Fleet Data Flywheel Infrastructure: Architect the framework for a continuous data flywheel that securely streams high-frequency kinematics, egocentric video, and force-torque telemetry from real-world robot fleets back into the training loop. Synthetic Data & Simulation Strategy: Define the strategy for generating high-fidelity, physics-aligned synthesized datasets using advanced simulation environments to scale training tokens for edge-case scenarios and long-horizon tasks. Data Compliance & Governance: Partner with operations, privacy, legal, and security teams to build enterprise-grade data management pipelines that programmatically enforce data minimization, anonymization, and CCPA/GDPR compliance. Data Quality & Token Curation: Implement automated telemetry filtering and dataset pruning strategies to identify high-value operational logs, eliminate redundant fleet data, and optimize training compute costs. Cross-Functional Physical AI Delivery: Act as the strategic bridge between machine learning research scientists, simulation developers, robotics engineers, and hardware teams to deliver data-ready platform features that improve physical reliability. About the team At Frontier AI & Robotics, we're not just advancing robotics - we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence - from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

Alexa International is looking for passionate, talented, and inventive Senior Applied Scientists to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. Senior applied scientists will drive cross-team scientific strategy, influence partner teams, and deliver solutions that have broad impact across Alexa's international products and services. Key job responsibilities As a Applied Scientist II with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs, particularly delivering industry-leading scientific research and applied AI for multi-lingual applications — a challenging area for the industry globally. Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains. The ideal candidate possesses a solid understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environment, like to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and are able to influence and align multiple teams around a shared scientific vision. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using advanced and innovative techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer’s location and language. * Quickly experiment and set up experimentation framework for agile model and data analysis or A/B testing. * Contribute through industry-first research to drive innovation forward. * Drive cross-team scientific strategy and influence partner teams on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems. * Lead end-to-end delivery of scientifically complex solutions from research to production, including reusable science components and services that resolve architecture deficiencies across teams. * Serve as a scientific thought leader, communicating solutions clearly to partners, stakeholders, and senior leadership. * Actively mentor junior scientists and contribute to the broader internal and external scientific community through publications and community engagement.

Do you want to define the multi-year science vision that transforms how millions of customers experience AWS products? Do you want to influence the AWS investment in GenAI technology and see the impact of your leadership moving the needle on billions of dollars of AWS business? Do you want to lead cross functional team that impacts multiple organizations (product, sales, marketing, finance) in AWS? Do you want to push the boundaries of AI/ML technology (e.g. multi-agent analytics system, agentic knowledge representation and management, graph neural networks, reinforcement learning, causal inference, optimization, and LLM-based forecasting models) to build scalable ML products that help AWS grow and delight our customers? The AWS Analytics Engineering (AAE) is at the forefront of leveraging cutting-edge AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their data. We are a cross functional org from decision science, ML products, data platform, and agentic analytics system. Our vision is to use artificial intelligence and machine learning to enable AWS product teams, product, and go to market leaders to drive product growth and create personalized, optimized, and simplified product experiences to delight our customers. We shape AWS product features (e.g. Console, Spot and Autoscaling), influence GTM efficiencies with customer propensity models, democratize data and insights access through multi-agent system, and influence AWS leaders’ product strategy. We are looking for a customer-focused, solutions-oriented Senior Applied Science Manager to lead and define the science and engineering strategy across AWS product organization. In this role, you will set the technical direction for agentic analytics products, build ML features for AWS products to optimize their operations, influence product growth related decision science for senior leaders, generate ML-driven sales leads for AWS GTM teams, innovate multi-agent analytics system, develop big data engineering system at the AWS data scale. You will partner directly with GMs, VPs, and senior product leaders from major AWS product management, marketing, and sales organization to translate complex business challenges into innovative scientific solutions that directly influence AWS's top line and bottom line. As a Senior Applied Science Manager , you will be the technical thought leader who establishes the science roadmap, analytics software development, drives cross-organizational alignment, and raises the bar for scientific rigor across the team. You will work cross organization from AWS product management, engineering, sales, marketing, and finance. You will operate effectively in ambiguous environments, exercise strong business judgment on high-impact decisions, drive the innovation and publication roadmap, and continuously push the frontier of what's possible with ML-driven product intelligence at AWS scale. Key job responsibilities Define and drive the multi-year science, ML product, and software engineering vision and roadmap for ML-powered product analytics across AWS Products, Marketing, and Sales organization - Build, lead, and develop a high-performing team of technical managers, applied scientists, and software engineers, including hiring top talent, managing performance, and growing careers through mentorship and promotion readiness - Partner with senior AWS leaders (GM/VP level) to identify strategic, data-driven opportunities and translate business objectives into high-impact scientific initiatives - Architect and guide enterprise-scale ML and agentic platform, including agentic system, knowledge representation, big data platform, deep learning, graph neural networks, reinforcement learning, causal inference, and forecasting models that predict business outcomes and enhance customer experiences - Manage cross-functional science and software engineering team to build ML driven products that are scalable and leading industry best practices at the AWS scale and speed - Invent, operationalize, and scale novel analytical frameworks and metrics that enable data-driven product growth and executive decision-making - Communicate findings, conclusions, and strategic recommendations to technical and non-technical business leaders across AWS - Mentor scientists and engineers, establish best practices for experiment design and model evaluation, and review technical artifacts to ensure quality - Identify and champion new science opportunities that expand AAE’s impact across AWS, building the case for investment and driving adoption A day in the life As a senior applied science manager in AAE org, you will shape the science strategy that underpins product decisions across multiple AWS organizations. You'll spend your time partnering with VPs and GMs to identify the highest-leverage science opportunities, architecting novel ML solutions to complex product challenges, and mentoring scientists across the team. You'll drive alignment across cross-functional stakeholders, ensure scientific rigor in our most critical initiatives, and communicate insights that directly influence AWS product roadmaps. You'll balance long-term vision-setting with hands-on technical leadership, diving deep into model architectures and data pipelines when needed while maintaining the strategic altitude to guide the team's direction. You will manage cross functional science and engineering team to build cutting edge agentic system and ML products that transform our product and customer experience. About the team We are a team of scientists and software engineers supporting AWS product leaders to make high-impact decisions through sophisticated analytical frameworks, trusted data science methods, and scalable ML products. We come from diverse backgrounds in statistics, computer science, engineering, and business analytics. We specialize in the full end-to-end ML development process, including data ingestion, ETL, model development, and model deployment in production. We provide AI/ML services across decision science, ML products, multi-agent analytics systems, and data engineering platform. High Impact Projects: We work on high-impact, high-visibility projects that directly influence AWS product roadmaps and senior leaders' decisions. Supportive Team Environment: We are proud of our supportive and inclusive team culture, we have each other's back during ups and downs. Work-Life Balance: We believe 80% of value comes from 20% of work, so we always prioritize our backlog ruthlessly based on business value. Learning Opportunity: Extensive opportunities to understand AWS business and leverage state-of-the-art AI/ML and cloud technology.