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

In Amazon Advertising, we apply machine learning at massive scale to optimize the prediction, ranking, and bidding behind every ad — deciding, in milliseconds, which ads to show shoppers and how to value them. We're looking for an Applied Scientist to help make sure the ads shoppers see are the right ones for them. You'll work across the science of how we rank, value, and bid on ads for Amazon DSP (Amazon's Demand-Side Platform) — including how we judge whether an ad is a good fit for the page a shopper is on and for the shopper themselves. It's high-scale, low-latency, customer-facing science: your models run live in front of millions of shoppers under tight real-time constraints. The questions are genuinely open — how do you tell whether an ad is relevant to someone, how do you balance what's good for shoppers, advertisers, and Amazon, and how do you keep getting that right as shopping behavior and inventory shift underneath you? Your work will have real impact, and you'll have room to shape where we take it. A few things make this stand out: your models touch a huge share of the ads shoppers see every day, so even small improvements add up fast; you'll run modern ML live under strict latency limits, across regions and very different types of ad inventory; and the problem space is rich — from how we value and bid on ads, to keeping models stable as traffic shifts, to what makes an ad a good fit for a shopper. Key job responsibilities - Design and improve the models that decide how ads are ranked, valued, and priced — including how relevant an ad is to the page and the shopper. - Apply and extend state-of-the-art techniques across e.g. ranking, deep learning, and information retrieval. - Own problems end to end: frame them, prototype, experiment, and ship them to production. - Balance competing objectives — shopper experience, advertiser and publisher value, and Amazon's business — into models that hold up across placements and marketplaces. - Communicate your work clearly to both business and science audiences, tailoring how you share it to each. - Write and ship your own production code backed by strong engineering support — we're all builders here. - Move fast with the best tools available, including modern AI coding assistants and agents. A day in the life You might start by digging into last week's experiment results, then use an AI coding agent to get your next prototype built and ready to test in production. In the afternoon you could be sketching a new way to measure ad relevance, reading a recent paper that bears on it, and talking it through with a senior scientist on the team. You'll move between hands-on science, writing and shipping real production code, and making the calls on your own work. About the team We're a group of scientists and engineers based in Edinburgh and London, working to make Amazon's ads more performant and relevant. We sit within a larger team spread primarily across New York City and the UK, and we have a broad mandate to build and experiment. You'll work alongside senior applied scientists you can learn from, with the data and infrastructure to do the work well and room to grow — with opportunities to attend top conferences (e.g., NeurIPS, KDD, ICML) and take on more scope over time.

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities - Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. - Perform data analysis and build data pipelines to drive business decisions. - Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. - Review and guide science solutions across the team. - Publish and socialize your and the team's research across Amazon and external avenues as appropriate - Leverage industry best practices to establish repeatable applied science practices, principles & processes.

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.

Do you want to leverage your expertise in translating innovative science into impactful products to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Core Science team would love to discuss how you can make that a reality. Our team is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world. In this role, you will spearhead science design and technical implementation innovations across our predictive modeling and forecasting work-streams. You'll enhance existing models and create new ones, empowering leaders throughout Amazon to make data-driven business decisions. You'll collaborate with scientists and engineers to deliver solutions while working closely with business stakeholders to address their specific needs. Your work will span various business domains (corporate, operations, safety) and analysis levels (individual, group, organizational), utilizing a range of modeling approaches (linear, tree-based, deep neural networks, and LLM-based). You'll develop end-to-end ML solutions from problem formulation to deployment, maintaining high scientific standards and technical excellence throughout the process. As an Applied Scientist, you'll also contribute to the team's science strategy, keeping pace with emerging AI/ML trends. You'll mentor junior scientists, fostering their growth by identifying high-impact opportunities. Your guidance will span different analysis levels and modeling approaches, enabling stakeholders to make informed, strategic decisions. If you excel at building advanced scientific solutions and are passionate about developing technologies that drive organizational change in the AI era, join us as we work hard, have fun, and make history. Key job responsibilities • Model Development & Innovation: Design and implement novel GenAI/LLM solutions using foundation models (e.g., Claude, GPT) and AWS services including Amazon Bedrock, SageMaker, and other AWS AI/ML tools • Research & Experimentation: Conduct applied research to advance the state-of-the-art in LLM applications, including prompt engineering, few-shot learning, fine-tuning, and model evaluation • Production Deployment: Build scalable, production-ready AI systems that serve millions of requests with high reliability, low latency, and cost efficiency • Cross-Functional Collaboration: Partner with product managers, engineers, and business stakeholders to translate business requirements into technical solutions and drive measurable impact • Technical Leadership: Mentor junior scientists, contribute to technical strategy, and establish best practices for GenAI development across the organization • Evaluation & Metrics: Design rigorous evaluation frameworks to measure model performance, bias, safety, and business impact • Documentation & Communication: Publish technical papers, create detailed documentation, and present findings to both technical and non-technical audiences About the team The People eXperience and Technology (PXT) Core Science Team uses science, engineering, and customer-obsessed problem solving to proactively identify mechanisms, process improvements, and products that simultaneously improve Amazon and Amazonians' lives, wellbeing, and value of work. As an interdisciplinary team combining talents from machine learning, statistics, economics, behavioral science, engineering, and product development, the Core Science team develops and delivers measurable solutions through innovation and rapid prototyping to accelerate informed, accurate, and reliable decision-making backed by science and data. We are building a talent intelligence layer — fusing natural language understanding, network science, and large-scale predictive modeling into a unified platform that continuously learns from how people work, collaborate, and grow across one of the world's largest and most complex workforces.

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.

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.

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.