Careers

Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.

We are seeking an Applied Scientist to join our AI Security team, which builds AI-based security analysis services that identify, validate, and help remediate vulnerabilities in Amazon source code and cloud configurations. The team combines large language models, security knowledge, code analysis, and security-engineer feedback to help builder teams detect security issues earlier in development and maintain security coverage across Amazon. As an Applied Scientist, you will research, model, design, and implement machine learning and agentic systems that reason over source code, infrastructure, and security context. You will collaborate with applied scientists, security engineers, software engineers, internal partners, and external researchers to improve finding precision, severity assessment, remediation quality, and continuous scanning coverage. Your work will enable continuous, AI-driven security analysis across Amazon’s software estate, converting source-code and infrastructure signals into validated, prioritized findings and proposed fixes that help builders prevent security defects from reaching production. Key job responsibilities • Research and develop accurate and scalable methods to solve our hardest AI security problems • Lead cross-functional work with applied scientists, software engineers, and security engineers to develop models and technical designs for AI systems that analyze source code, validate security findings, and generate remediation guidance at Amazon scale. About the team As an Applied Scientist on the AI Security team, you will research, develop, evaluate, and productionize models that identify security vulnerabilities in source code and cloud configurations. You will design experiments, build evaluation datasets and metrics, and apply large language models, program analysis, and security knowledge to improve finding precision, severity assessment, and remediation quality. You will work with applied scientists, security engineers, and software engineers to deliver systems that convert security signals into validated, prioritized findings and proposed fixes for builder teams across Amazon. Your work will shape how Amazon identifies and addresses security defects throughout the software development lifecycle. You will investigate model behavior, analyze failure modes, develop methods to improve recall and precision, and measure the impact of new capabilities before deployment. The role provides opportunities to solve applied machine learning problems across source code, infrastructure, cloud services, and security operations. About the team The AI Security team develops systems that analyze source code and cloud configurations to identify, validate, and help remediate security vulnerabilities. We combine large language models, security engineering, automated evaluation, and feedback from builders and security engineers to improve security coverage across Amazon. The team works on problems including vulnerability detection, severity assessment, false-positive reduction, remediation generation, continuous scanning, and security-data analysis. Diverse Experiences Amazon Security values varied professional and life experiences. We encourage candidates from traditional and nontraditional career paths, including candidates early in their careers and candidates with alternative technical backgrounds, to apply. Why Amazon Security? Security is central to customer trust and Amazon’s products and services. Our organization establishes and maintains security standards across Amazon. Team members build experience across cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture Amazon Security brings together people with different experiences, technical backgrounds, and perspectives. We invest in learning opportunities and create space for teams to examine security problems from multiple viewpoints. Training and Career Growth The team provides knowledge-sharing, technical training, mentorship, and opportunities to work across security domains. Applied Scientists can deepen their expertise in machine learning, security research, evaluation systems, and production AI services. Work/Life Balance We support work-life harmony and flexible working practices. Teams plan work with sustainable delivery in mind and support employees in meeting responsibilities at work and at home.

Amazon Ads is one of the fastest-growing businesses at Amazon, helping millions of advertisers — from first-time sellers to global agencies — reach customers at every stage of the shopping journey. Advertising Services' vision is that Amazon Ads is the ultimate partner for advertising customers. We deliver on this vision by building the internal tooling, automations, and platforms that enable efficient service delivery at scale — ensuring every advertiser, regardless of size or sophistication, has access to world-class support. Are you excited about applying AI and machine learning to transform how a massive services organization operates? The Ad Services Engineering team builds and operates the systems that underpin the full spectrum of advertiser services — from self-service support experiences (Help content, Contact Us traffic patterns, knowledge bases) to managed campaign operations. We're investing in applied science to fundamentally change how advertisers get help, how campaigns get launched, how service teams find answers, and how we detect and resolve operational issues at scale. On the self-service side, we study patterns in Contact Us traffic, experiment with improving Help content effectiveness, and optimize the knowledge systems that power advertiser self-resolution. On the managed services side, we're building autonomous agents that handle low- and medium-judgment tasks currently performed by humans — freeing up Amazonians to focus on higher-value, strategic work. Across both, we're developing intelligent assistants and pioneering automated identification of product gaps from unstructured operational data — turning thousands of data points into actionable insights that drive product improvements. As a Sr. Applied Scientist, you will be the science leader for AI-powered automation and intelligence within Advertising Services. You'll own the research and development of the machine learning systems that improve self-service advertiser experiences, power autonomous workflow agents, and drive operational intelligence — directly translating into higher self-service resolution rates, productivity gains measured in tens of thousands of hours annually, and measurable improvements in advertiser experience.

Amazon serves hundreds of millions of customers. Each one has a unique history of purchases, preferences, and behaviors. Our team's mission: turn that history into real-time contextual intelligence that makes every Amazon experience feel personal. We're hiring an Applied Scientist to push the boundaries of what's possible with LLMs, semantic retrieval, and customer understanding at scale. The problem space: Imagine a system that can instantly synthesize years of customer signals — what they bought, what they love, what they're planning — and surface the exact right context for any experience, in milliseconds. That's what we build. It's equal parts information retrieval, generative AI, and systems engineering. Why this role: 1. Scale: Your models will serve 1,500+ requests per second across Amazon's largest surfaces. 2. Impact: Direct revenue attribution in the hundreds of millions — your work shows up in customer experiences the same week. 3. Frontier tech: Fine-tuning LLMs, building custom embedding models, designing retrieval architectures that balance quality with sub-100ms latency constraints. 4. Data richness: Access to one of the most comprehensive customer behavior datasets anywhere. 5. Ownership: End-to-end — from research to production deployment to metric evaluation. Key job responsibilities 1. Invent new approaches to contextual retrieval, relevance scoring, and LLM-based summarization. 2. Fine-tune and evaluate language models for domain-specific understanding. 3. Design experiments that measure real customer impact, not just benchmark scores. 4. Ship production systems and iterate based on live metrics. 5. Collaborate across teams — Alexa, Search, Recommendations — as a platform that powers them all. 6. Mentor team members and shape the technical direction of our roadmap. Please visit https://www.amazon.science for more information. A day in the life You'll analyze large-scale behavioral data, design experiments, and build models that ship to production. You'll work closely with engineers to ensure your science translates into low-latency, high-reliability systems. You'll present findings to leadership and influence product strategy. Some weeks you'll be deep in model architecture; other weeks you'll be debugging a relevance gap in production. Every day, your work reaches real customers. About the team We're a small, high-impact team that values scientific rigor and engineering craft equally. The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.

As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities Thought leader on the team and help set team directions Research multiple problem domains, suggest various approaches to try and be as hands-on as needed while providing more junior scientists with critical mentorship Collaborate with engineers to come up with the right LLD and HLD to solve key business problems Strong emphasis on communication via writing, internal and external talks, and being able to align with multiple stakeholders A day in the life As an Applied scientist II, a typical day will involve aligning with key product, engineering and business stakeholders ; advising junior scientists on the work they are doing ; reading current research papers and staying up-to-date on AI research ; diving deep as needed to improve CMT models and addressing stakeholders from the science perspective ; writing python code

The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required.

We are looking for passionate, talented, and inventive Principal Applied Scientist with a strong machine learning background to help build industry-leading Conversational AI Systems. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), Dialog Systems including Generative AI with Large Language Models (LLMs) and Applied Machine Learning (ML). As part of our team, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use language technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of human language technology: NLU, Dialog Management, Conversational AI, LLMs and Generative AI. A day in the life The team uses generative AI and foundation models to reimagine the experience of all customers on AWS. We explore new technologies and find creative solutions. Curiosity and an explorative mindset can find a place here to impact the life of engineers around the world. If you are excited about this space and want to enlighten your peers with new capabilities, this is the team for you.

Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is seeking an innovative and driven Applied Scientist to join our engineering and science hub in Bangalore. You will work alongside a world-class team of machine learning experts to break new ground in understanding user intent, classifying complex audio and musical forms, and creating next-generation interactive search experiences that help users find the exact music, podcasts, and audio content they are in the mood for. In this role, you will own the design, development, and deployment of end-to-end machine learning systems. You will balance execution on core search and discovery priorities—such as improving retrieval accuracy, latency, and relevance for millions of daily queries—while laying the foundational modeling capabilities for broader semantic understanding and advanced conversational search experiences across mobile, web, and voice-forward devices (like Alexa and Echo). Key job responsibilities - Core Search & Execution: Collaborate with scientists, software engineers, and product managers to define, frame, and solve complex business and ranking problems as machine learning, information retrieval, or optimization tasks. - Advanced AI & Modeling: Design, build, train, and evaluate production-grade ML models using classical machine learning, deep learning, Large Language Models (LLMs), and Agentic AI techniques to scale music discovery and intent resolution. - End-to-End Production Ownership: Take algorithms from research ideation to production deployment. Build scalable data pipelines, efficient model-serving systems, and robust offline/online evaluation frameworks. - Experimentation & Iteration: Design and analyze large-scale A/B experiments across millions of customers to measure impact on search relevance, engagement, and customer satisfaction, refining models for continuous improvement. - Forward-Looking Innovation: Research and implement novel statistical and machine learning approaches, exploring multi-modal understanding, rich content semantics, and advanced retrieval mechanisms that extend beyond traditional search boundaries. - Technical Communication: Communicate findings, architectural decisions, and technical roadmaps clearly to both technical peers and executive stakeholders, authoring robust design documents and contributing to team standards. Basic Qualifications - PhD, or Master’s degree and 4+ years of relevant experience in Computer Science, Computer Engineering, Machine Learning, Statistics, or a related quantitative field. - 3+ years of hands-on experience building machine learning models or algorithms for business applications and deploying them into production. - Strong programming skills in Python, Java, C++, or related languages, with a solid foundation in data structures, algorithms, and object-oriented design. - Experience in one or more of the following areas: Information Retrieval, Natural Language Processing (NLP), Recommender Systems, Deep Learning, or Numerical Optimization. - Demonstrated ability to work effectively with cross-functional teams in a fast-paced environment. Preferred Qualifications - Experience with large-scale distributed computing frameworks and big data systems (e.g., Spark, Hadoop, AWS infrastructure). - Experience building search ranking, query understanding, or semantic retrieval systems for high-scale consumer applications. - Familiarity with modern foundation models, LLMs, fine-tuning techniques, and efficient inference optimization for production services. - Track record of peer-reviewed publications or patents at top-tier machine learning/AI conferences (e.g., NeurIPS, KDD, ACL, SIGIR, ICML). - Experience in designing, executing, and evaluating rigorous online A/B experiments.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is looking for an execution-focused Senior Applied Scientist to spearhead core scientific initiatives within our Bangalore hub. In this leadership-by-example role, you will define and execute the applied science roadmap for search and content discovery systems, directly impacting millions of customers worldwide. You will operate at the exciting intersection of large-scale search infrastructure, applied machine learning, and foundation models. You will drive immediate, high-impact business deliverables in music search relevance, personalization, and retrieval performance, while simultaneously architecting the long-term technological vision that expands our search ecosystem toward deeper semantic intelligence, agentic workflows, and cross-domain audio understanding. Key job responsibilities - Strategic Roadmap & Architecture: Define and execute the technical and scientific roadmap for music search systems, making critical architectural decisions that balance short-term feature delivery with long-term scalability, maintainability, and extensibility. - Pioneering Applied Science: Lead the design and implementation of state-of-the-art machine learning solutions leveraging deep learning, LLMs, and agentic workflows to solve complex search, ranking, and intent-matching challenges. - Cross-Functional Leadership: Partner closely with product management, engineering leaders, and peer teams to harmonize technical direction and deliver synchronized customer experiences. - Technical Excellence & Mentorship: Drive engineering and scientific excellence across the team by conducting rigorous design reviews, establishing modeling best practices, setting high bars for artifact delivery, and mentoring junior/mid-level scientists. - Experimentation & Scaling: Establish robust scientific processes for large-scale data analysis, offline model validation, and online A/B experimentation, ensuring high statistical rigor and measurable business impact across millions of active listeners. - Stakeholder Influence & Writing: Author strategic whitepapers, and executive-level documentation. Communicate complex technical options and trade-offs to senior leadership to drive informed decision-making. Basic Qualifications - PhD, or Master’s degree and 6+ years of applied research and industrial machine learning experience in Computer Science, Machine Learning, or a related field. - 3+ years of specialized experience designing, building, and scaling machine learning models for core production business applications (e.g., Search, Recommendation Systems, or Large-Scale Information Retrieval). - Expert programming proficiency in Python, Java, C++, or related languages, combined with deep familiarity with neural network architectures and deep learning frameworks. - Proven track record of owning end-to-end technical deliverables from problem formulation and model architecture to production deployment and performance tuning. - Demonstrated leadership in mentoring technical talent and driving engineering/scientific best practices. Preferred Qualifications - Deep expertise in search retrieval, query understanding, ranking algorithms, and large-scale vector search/embedding systems. - Experience building applied science solutions on top of foundation models, large language models (LLMs), or multi-modal architectures. - Experience with large-scale distributed training and inference optimization on cloud infrastructure (AWS). - A strong publication record or patent portfolio in top-tier peer-reviewed venues (e.g., NeurIPS, SIGIR, KDD, ACL, ICML). - Experience designing and interpreting complex online experimentation frameworks for consumer-facing recommendation or search products.