Careers

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs), ASR, TTS, and Speech to Speech models, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist 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 Large Language Models (LLMs), ASR, TTS, and Speech to Speech model. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. 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. * Drive research in ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Advance multilingual speech recognition and synthesis using LLM-based architectures * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Collaborate with partner teams on evaluation frameworks and post-training methodologies. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs), ASR, TTS, and Speech to Speech models, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist 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 Large Language Models (LLMs), ASR, TTS, and Speech to Speech model. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. 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. * Drive research in ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Advance multilingual speech recognition and synthesis using LLM-based architectures * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Collaborate with partner teams on evaluation frameworks and post-training methodologies. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.

As an Applied Scientist on the Science SW team, you will be a versatile generalist who collaborates closely with other scientists and engineers teams to bring research to production across a broad portfolio of problems: from computer-vision perception platforms to building-wide optimization and orchestration. This role combines the scientific application of ML and applied mathematics with a strong product focus. It will be your job to frame ambiguous business problems as tractable scientific problems, and to implement novel ML systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments. Key job responsibilities • Own the research and development of scientific and ML solutions across a broad range of problems spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed / first-principles modeling in a production environment. • Rapidly ramp on unfamiliar problem domains, frame ambiguous or open-ended business problems as tractable scientific problems, and prototype solutions end to end. • Prototype and evaluate sensing hardware and lightweight, edge-deployable models that run on commodity compute under real-world constraints. • Collaborate across multiple science and engineering teams to integrate your solutions into our deployment architecture. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs), ASR, TTS, and Speech to Speech models, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist 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 Large Language Models (LLMs), ASR, TTS, and Speech to Speech model. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. 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. * Drive research in ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Advance multilingual speech recognition and synthesis using LLM-based architectures * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Collaborate with partner teams on evaluation frameworks and post-training methodologies. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.

We are seeking a seasoned executive leader to join our Network Solutions team within Amazon Customer Service, as Director, Data Science. In this role you will build and lead a data, analytics, and measurement science function that orchestrates Amazon's Customer Service full network – across our human-assisted and customer-facing automation AI-enabled channels – as a single intelligent system. Network Solutions owns demand forecasting, network planning, routing, real-time observability, and workforce strategy, and is building the next generation of AI-enabled planning systems with intelligent and adaptive capabilities that consider humans and AI-agents, simultaneously. You will lead a multi-disciplinary team – scientists, data engineers, business intelligence engineers, and analysts – who will build the data strategy these new systems require and the measurement science that reshapes how every network decision is made. At the core is a unified measurement framework that measures the value of experiences across customers, associates, and our business, drawing on causal inference, economics, operations, and behavioral science. You will create the underlying logic for how we plan capacity, route customers, develop associates, and allocate resources – and serve as an interface and work closely with partner teams within Customer Service, including the CS-wide Data Intelligence organization. The ideal candidate brings deep cross-disciplinary experience in data science, measurement, and operations – a track record of building and leading data and insights organizations from 0-to-1, the scientific depth to drive novel measurement frameworks, and the rare ability to blend quantitative rigor with an understanding of human systems – developing the right logic to drive decision-making at scale. Key job responsibilities - Build and lead the Network Solutions data and analytics organization: provide unified leadership across business intelligence engineering, data engineering, analytics, and reporting; recruit, grow, and retain a team spanning measurement scientists, data engineers, and analysts, building the function from 0-to-1 - Drive the measurement science and decisioning core: develop the causal inference-based framework that quantifies value creation across multiple dimensions; establish methodology standards, signal definitions, and utility function design that give Network Solutions a unified view of performance - Own the Network Solutions data foundation: own critical signal pipelines, govern data quality, and build a composable data architecture that all Network Solutions product and science teams build on; ensure measurements are interoperable across planning, routing, incident management, and workforce systems - Integrate measurement science into production systems: demonstrate the shift to value-informed decision-making; partner with product and engineering teams to embed measurement frameworks into planning, routing, and resource allocation - Interface with Amazon CS and company-wide data, analytics, and science organizations: represent Network solutions as a peer partner to central data infrastructure, customer experience measurement, and applied science functions; consolidate Network Solutions data demand into a clear, prioritized voice - Drive adoption of data standards and measurement frameworks: translate complex models and multi-dimensional utility functions into intuitive, actionable insights for non-technical stakeholders; establish shared measurement standards that teams across the organization can build on - Develop the long-term data and science roadmap: anticipate future needs as the organization scales; identify opportunities to extend measurement capabilities beyond Network Solutions About the team Network Solutions sits at the intersection of customer experience and associate experience within Amazon Customer Service, owning the logic and infrastructure that determines how those two sides are balanced, served, and optimized together. On one side is the customer: their experience, their journey, and the AI automation that increasingly shapes both. On the other side is the associate: their work, their well-being, and the conditions that make them effective. Network Solutions sits at the center, owning demand forecasting, network planning, routing and matching, real-time observability, and workforce strategy – including the long-term design of the network itself as the balance between AI and human engagement continues to evolve. We are building the next generation of systems that will transform how Customer Service operates using intelligent, adaptive capabilities that learn and improve continuously.

Do you thrive in generating actionable insights at scale from complex datasets? Can you use data to look around the corners of a business and guide leaders on impactful business decisions? The Shop and Keep Data and Science team builds internet-scale shopping intelligence that helps Amazon customers find what they want with ease and give them the right information to make confident and lasting purchases. Our work powers customer experiences across Amazon.com surfaces, including Search, Product Detail Pages, Recommendations and Ads. We are looking for an experienced Senior Data Scientist to lead high visibility science initiatives that guide the business and ensure quality of our shopping intelligence signals. This role will focus on Data Science and Analytics related to signal quality evaluation, impact measurement, and delivering business insights. The role requires deep technical skills, strong business acumen and a deep analytical background to provide actionable data-driven insights and decision support. As a member of this team, the role will dive deep into our data and leverage their strong skills in measurement science, LLMs, predictive and prescriptive models, data ETLs, dashboards and visualization to support business strategy and deliver goals with executive visibility. You must have the ability to communicate effectively across multiple technical and non-technical business units, as well as across other geographies. Successful members of this team collaborate effectively to solve data problems, are highly organized and detail-oriented, implement new solutions for complex problems, and deliver successfully against highest standards. The ideal candidate will be a highly motivated individual that seeks to “tell the story” of the data with little direction or supervision. Key job responsibilities • Leverage state-of-the-art measurement science approaches, including LLMs, to develop evaluation frameworks for a variety of signals and customer experiences. • Support the development of continuously-evolving business analytics and data models, own the quantitative analysis of the performance of our features. • Continually develop new ways of using data to look around the corners and guide the development of shopping intelligence signals. • Use LLMs, machine learning, data mining, and statistical techniques to design/run experiments that solve complex business problems. • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. • Develop a deep understanding of e-commerce metrics, reporting tools, and data structures in order to identify and drive resolution of issues, provide actionable intelligence with existing metrics or identify, develop, and propose new metrics, dashboards, scorecards or new tools. • Develop relationships and processes with partner teams, PMs/TPMs, engineers, and other functional teams to identify and address reporting issues. • Manage and develop advanced analytical tools that align, and simplify, monthly business reviews, annual planning, operations and forecasting processes, based on the needs of the business and stakeholders. About the team The Shop and Keep Data and Science team's mission is to build shopping intelligence that helps Amazon customers find what they want with ease and give them the right information to make confident and lasting purchases. Through our personalized discovery and product evaluation signals, we assist customers in their shopping missions as they navigate Amazon.com's surfaces. We also share a selection of signals with partner teams and sellers to establish closed loop mechanisms and drive improvements in our signals, customer outcomes and Amazon’s product selection.

Are you interested in big data, Machine Learning, and building recommendation services using Generative AI? If so, Amazon's Personalization team might be the right place for you. Key job responsibilities - Use AI and machine learning to create scalable solutions for business problems. - Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes. - Design, develop and evaluate highly scalable models for predictive learning. - Work closely with software engineering teams to drive model implementations and new feature creations. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Research and implement novel machine learning and statistical approaches - Review peer work and provide feedback A day in the life You are an Applied Scientist who loves big data and passionate about improving customer shopping experience by inventing and applying state-of-art technologies (e.g., LLMs/SLMs, Machine Learning, Natural Language Processing, and Computer Vision) to build the next-generation product recommendation engine for Amazon. You have an entrepreneurial spirit, know how to deliver, are deeply technical and highly innovative. You work closely with software engineers to put algorithms into production. You also work in partnership with teams across Amazon to create enormous benefits for our customers. About the team We are part of Amazon’s Personalization organization, a high-performing group with a huge impact on hundreds of millions of customers, innovating at the intersection of customer experience, machine learning, and large-scale distributed systems. We run global experiments and our work has revolutionized e-commerce with features such as "Compare with similar items", "Keep Shopping For", “Customers who bought this item also bought”, and, “Frequently bought together” among others.

Amazon's Search team creates ML algorithms that connect customers around the world with products that delight them. We harness machine learning at Amazon's scale to make the customer experience easier and smoother. Our impact is large. For example, if your innovations save even 1 minute per customer per year, then for every 100 million customers, you save approximately 190 years of human effort. Key job responsibilities As an Applied Scientist on the Search Ranking team, you will build search ranking models that work for thousands of product types, billions of queries, and hundreds of millions of customers spread around the world. You will find the next set of big improvements to ranking, leverage large datasets to understand the complexities of customer behavior, and build ML models that work at Amazon scale. Amazon's Search ranking relies on efficient early stage ranking followed by power final stage ranking models. this role focuses on efficient models for early stage ranking phase, and techniques to personalize these models and optimize them automatically. A day in the life Our primary focus is improving search ranking systems. On a day-to-day this means building ML models, analyzing data from your recent A/B tests, and collaborating with partner teams on joint goals. We leverage agentic workflows to improve our scientific velocity, and partner closely with engineering teams to bring our ideas to production. You will shape the future of search ranking by presenting proposals to Amazon's business and tech leaders. About the team We are a team consisting of ML scientists and software engineers. Our interests span machine learning for better ranking, personalization to refine the ranking of our models, reinforcement learning to bring the benefits of exploration to search ranking, and infrastructure to make it all happen at scale and efficiently.

Do you want to shape the future of how Alexa+ enabled devices connect with Alexa Cloud? Do you want to be part of a team that builds solutions used by millions of customers? If you said yes to any or all of those, come join the Alexa Connected Devices! We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs and Recommender Systems, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.

Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities Strategic Leadership & Vision - Define long-term science vision for Traffic Quality driven by advertiser and publisher needs, translating direction into actionable team plans. - Solve strategically important business problems independently, delivering robust, scalable scientific solutions with limited guidance. - Proactively identify technology gaps and business opportunities, determining resource allocation priorities. Scientific Innovation & Execution - Design and implement statistical and machine learning solutions to detect robotic and human traffic patterns across billions of daily ad events. - Own full development cycle for production-level code handling billions of ad requests: design, prototype, A/B testing, and deployment. - Stay current with scientific advancements and build publication strategy while championing excellence best practices. - Directly protect hundreds of millions of dollars in advertiser spend annually while maintaining seamless user experience. - Partner with engineers, product managers, and cross-functional teams to solve complex IVT detection problems and influence strategic initiatives. - Mentor scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)