Currently tracking 1110 active AI roles, down 16% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$465k (avg $194k).
Amazon has 1472 active AI-related job listings. The company is heavily focused on roles within the "agents" stage, which accounts for 38% of its AI hiring, followed by "application" at 26%. Engineering is the dominant function, with 1172 positions. Over the last 30 days, Amazon has added 667 new AI roles, representing a 74% increase compared to the previous 30-day period. Frequent tech tags include agent_orchestration, model_serving, and multimodal.
Amazon currently has 1573 active AI-related roles in our index. The most common open titles are: ML Data Associate-II (9), 2026 Applied Scientist Intern, Amazon University Talent Acquisition (8), AI Data Associate (Dutch) , Artificial General Intelligence Data Services (8), Software Development Engineer, AWS (8), Senior Delivery Consultant - Data , Professional Services, AWSI HCLS (7). Most positions are in Engineering and Research.
Amazon's active AI hiring is concentrated in: agents (41%), application (26%), serving infrastructure (13%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Amazon is hiring AI talent in: United States (1023 roles), Canada (59 roles), United Kingdom (47 roles), India (23 roles).
Job postings at Amazon most frequently mention: Machine Learning, Generative AI, Large Language Models (LLMs), Software Engineering, Agentic Systems.
In the past 30 days, Amazon has posted 696 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Senior Applied Scientist, AWS Marketplace Science Senior Applied Scientist role at AWS Marketplace focusing on building advanced AI/ML systems for information retrieval and agentic systems. The role involves translating scientific research into practical, scalable solutions, driving evaluation, and collaborating with product and engineering teams. It emphasizes experience in large-scale B2B platforms, generative AI, and agentic systems, with a focus on shaping purchasing decisions. | AgentServe | 8 |
| Software Development Manager, AWS Neuron SDK - Distributed Training This role focuses on engineering and optimizing distributed training for large-scale ML models, particularly LLMs with multi-modal inputs/outputs, on AWS Neuron accelerators. The primary goal is to enhance training resiliency and performance across thousands of nodes, ensuring Trainium devices are first-class citizens for ML acceleration. |
| DataServe |
| 8 |
| Software Development Manager, Amazon Quick Software Development Manager for a new generative AI-powered assistant initiative within Amazon Quick. The role involves leading a team to define, drive, and execute product vision, invent and ship software impacting end customers, and collaborate with scientists and product managers. Requires experience in engineering team management, system design, and shipping production software, with a preference for Gen-AI development and LLM application engineering. | Agent | 8 |
| Applied Science Manager, Healthcare AI Applied Science Manager for Healthcare AI at AWS, leading teams to build innovative AI-powered healthcare solutions using generative and agentic AI. Focuses on transforming clinical and administrative workflows, improving patient care, and enhancing operational efficiency. The role involves technical leadership, people management, and driving innovation in areas like medical administration reasoning, AI agent design, and medical coding, with a goal of translating research into production for customer-facing products. | ShipAgent | 8 |
| Senior Applied Scientist, Sponsored Products and Brands Senior Applied Scientist role at Amazon Ads, focusing on developing and applying generative AI and LLM technologies to enhance sponsored products and brands advertising. The role involves inventing new advertiser and shopper experiences, building monetization and optimization systems, defining long-term science vision, and bringing state-of-the-art GenAI models to production. Requires strong ML, LLM, and GenAI expertise, with experience in model fine-tuning, prompt engineering, and digital advertising technology. The position also involves leading scientific rigor, mentoring talent, and communicating with leadership. | ShipPost-train | 8 |
| Applied Scientist, Traffic Quality Applied Scientist role focused on detecting sophisticated invalid traffic (IVT) in advertising using deep learning, self-supervised techniques, representation learning, and advanced clustering. The role involves working with massive datasets, developing novel algorithms, and deploying production-quality ML components. Emphasis on research, innovation, and contributing to the scientific community through publications. | Agent | 8 |
| Data Scientist, Traffic Quality The Data Scientist II in Traffic Quality at Amazon Ads will focus on applying advanced statistical techniques and machine learning to detect sophisticated invalid traffic (IVT) in advertising. This role involves defining new research problems, applying ML models to detect fraud, performing data analysis, working with massive datasets, and producing research reports. The team leverages deep learning, generative modeling, user behavior analysis, anomaly detection, and time-series analysis to protect advertiser spend and maintain marketplace integrity. The role also involves mentoring junior scientists and has a strong publication record. | AgentData | 8 |
| Applied Scientist, Regulatory, Intelligence, Safety and Compliance (RISC) Applied Scientist role focused on agentic AI, GenAI, and Machine Learning for regulatory compliance at Amazon. The role involves designing and evaluating state-of-the-art algorithms for content generation, multi-modal classification, intent detection, information retrieval, anomaly detection, and agentic systems. It requires developing and deploying ML models at scale, with an emphasis on scientific innovation and publication. | AgentPost-train | 8 |
| Applied Science Manager, Safe Autonomy, SAF Lab Manager for a team developing a universal safety layer for robotic systems, focusing on safe autonomy for dynamic robots. The role involves leading research, strategy, and ensuring innovations translate into production systems at Amazon scale, integrating control barrier functions with planning, perception, and learning. The team publishes research and collaborates with academic and industry partners. | ShipAgent | 8 |
| Senior Software Development Engineer, AWS Mantle Senior Software Development Engineer to build and scale the distributed inference engine for Amazon Bedrock, powering enterprise access to foundation models globally. The role involves designing, building, and operating high-performance systems for ML inference at massive scale, focusing on request routing, load balancing, model lifecycle management, and performance optimization across AWS regions. | Serve | 8 |
| Machine Learning Engineer , Amazon Customer Service Machine Learning Engineer on the Data Intelligence team within Amazon Customer Service, responsible for designing and building scalable AI/ML systems, end-to-end AI pipelines, and production-grade AI services including generative AI, LLMs, and intelligent agent systems. The role involves building infrastructure for the complete AI model lifecycle, handling high-volume inference, implementing AI governance, and developing AI-powered products. | AgentServe | 8 |
| Applied Scientist, Amazon Optics The Applied Scientist will design and develop ML models for physical security operations, including automated alarm triage, false alarm suppression, and anomaly detection. They will build LLM-based systems for querying and summarizing incident data, develop predictive models for security patterns, and research computer vision applications for threat detection. The role involves architecting ML pipelines, defining evaluation frameworks, and owning model performance in production. They will also drive scientific breakthroughs in multi-modal fusion, few-shot learning, and reinforcement learning, and collaborate with software engineers and cross-functional stakeholders to integrate ML solutions into the Optics platform. | AgentData | 8 |
| Sr GenAI Infra Specialist SA, AWS WWSO Startup Senior GenAI Infrastructure Specialist SA for AWS WWSO Startup team, focusing on AI infrastructure for model training and inference optimization. The role involves advising startup customers on hardware, optimization techniques, and deploying strategies for large-scale AI workloads on AWS. | ServePost-train | 8 |
| Applied Scientist II- Recruiting AI Agents, Recruiting Agents & Candidate Voice The Applied Scientist II will design, implement, and deploy AI-powered agentic solutions for Amazon's recruiting process. This role involves creating conversational AI agents using LLMs and GenAI, developing evaluation frameworks, and collaborating with cross-functional teams to integrate these solutions into candidate-facing platforms. The position also requires staying current with agentic AI research and contributing to the scientific community. | Agent | 8 |
| Senior Applied Scientist, AWS Security Senior Applied Scientist role focused on building and deploying AI/ML systems for cybersecurity threat detection and mitigation within AWS. The role involves analyzing threat data at scale, developing prototypes, and leading technical innovation to protect AWS customers. | ShipServe | 8 |
| Principal Solutions Developer , Prototyping and Customer Engineering (PACE) Principal Solutions Developer role focused on building prototypes and customer engineering solutions using AI, Generative AI, and Agentic Design within enterprise contexts on AWS. The role requires a strong builder mentality, technical depth, and customer-facing skills to explore new technical grounds and accelerate AWS adoption. | Agent | 8 |
| Applied Scientist I, Sponsored Products and Brands Agent The role focuses on building a personalized and context-aware agentic advertiser guidance system that integrates LLMs with tooling across various Amazon Ads platforms. The goal is to provide strategic product guidance and granular optimization through agent architectures, tool integration, and model customization. | Agent | 8 |
| Applied Scientist I, Buyer Risk Prevention (BRP) Applied Scientist role focused on building and deploying machine learning models for fraud and risk prevention in an e-commerce environment. The role involves end-to-end ownership of ML systems, leveraging large datasets, and applying Generative AI/LLMs to enhance detection and prevention capabilities. Collaboration with engineering and business stakeholders is key, with an emphasis on scalable solutions and performance monitoring. | Ship | 8 |
| Data Scientist II, RufusX Science UK Data Scientist II role focused on developing and optimizing AI-driven conversational and multimodal shopping experiences using NLP, ML, and large language models. The role involves analyzing large datasets, building predictive models, designing experiments, and collaborating with scientists and engineers to launch features and systems. It touches on inference infrastructure and agentic systems for shopping tasks. | AgentServe | 8 |
| Data Scientist II, Alexa for Shopping Science UK Data Scientist II role focused on building and optimizing AI-driven conversational shopping experiences for Alexa. The role involves developing multimodal systems using NLP, ML, and LLMs, with a focus on agentic capabilities, information retrieval, recommender systems, and knowledge graphs. Responsibilities include data analysis, modeling, experimentation, A/B testing, metric development, and collaboration with scientists and engineers to launch features and systems. The role also touches on inference and serving infrastructure. | AgentServe | 8 |
| Applied Scientist II, Alexa Ads The Applied Scientist II role at Amazon's Alexa Ads team focuses on building Generative AI powered agentic advertising products and personalization models for the Alexa consumer assistant. This involves designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating with engineers to deploy models into production. The role is part of a greenfield team aiming to rethink ad ranking, pricing, and personalization for voice-first and screen-first surfaces, with opportunities for both production deployment and top-tier publications. | AgentServe | 8 |
| Applied Scientist III- Recruiting AI Agents, Recruiting Agents & Candidate Voice This role focuses on designing, implementing, and deploying AI-powered agentic solutions for Amazon's talent acquisition process. The scientist will leverage LLM and GenAI technologies to create conversational AI agents that guide candidates through the hiring journey, and will develop evaluation frameworks to measure agent effectiveness and user experience. Collaboration with cross-functional teams and staying current with agentic AI research are also key responsibilities. | Agent | 8 |
| Software Development Manager, Analytics and Data Management Software Development Manager leading a team to build and scale a conversational AI assistant (SpektrBot) for interacting with a large advertising data lake. The role involves managing engineers, driving execution on core products like RAG pipelines and multi-agent architectures, ensuring operational excellence, and coordinating with various partner teams to deliver AI-powered data insights through natural language. | AgentServe | 8 |
| Senior Applied Scientist, Industrial Robotics Group Senior Applied Scientist role focused on developing AI and ML systems for industrial robotics and manufacturing. The role involves creating real-time decision systems, inventing new algorithms, and delivering complex solutions into production, with a strong emphasis on optimization and ML techniques applied to manufacturing flow and throughput. | ShipAgent | 8 |
| Sr Manager, Applied Science, Creative Intelligence This role leads a science organization focused on Dynamic Creative Optimization (DCO) and Creative Brain (CB) for Amazon Ads. The core responsibility is to own and drive the models, algorithms, and research that personalize ad creative in real-time to improve advertiser performance, focusing on conversion and long-term value. The role involves managing a team of applied scientists and data scientists to build a closed-loop system that continuously learns from each impression to improve creative decisions. Key objectives include compressing learning loops, expanding optimization coverage, solving cold-start problems, building persistent memory layers, developing causal inference for creative components, creating cross-advertiser priors, designing representation architectures for creative quality reasoning, and owning quality science (defect detection, compliance, aesthetics). The role also involves defining science strategy, leading competitive analysis, ensuring rapid translation of research to production, and managing a team of 8+ scientists. | ShipServe | 8 |
| Applied Scientist, EU INTech Consumer Selection Discovery, NintAI Applied Scientist role focused on building and deploying AI/ML models for Amazon's global search and discovery experiences, aiming to improve customer navigation and product discovery. The role involves end-to-end ownership from problem analysis and science plan design to production deployment, with a focus on ranking, computer vision, and generative AI. | Ship | 8 |
| Applied Scientist Applied Scientist role at Audible focusing on developing and productionizing ML/AI models for various applications including NLP, RL, and Generative AI. The role involves inventing scientific approaches, building scalable solutions, and collaborating with engineering and product teams to deliver customer-facing features and foundational capabilities. | Ship | 8 |
| 2027 Applied Science Intern (Machine Learning, Recommender Systems), Amazon International Machine Learning Internship role focused on developing novel solutions and prototypes in Deep Learning and Generative AI, with potential to deliver to production. Collaborates with R&D scientists and aims to publish research in top ML conferences. | Ship | 8 |
| Applied Scientist, FinTelligence This role focuses on building and deploying generative AI applications within Amazon's FinTech organization. The primary responsibility is to create AI systems and autonomous agents that process financial transactions, extract intelligence from documents, and learn from user interactions. The role involves solving inference at scale, developing robust evaluation frameworks, and ensuring the AI systems are trustworthy and compliant for finance teams. The work spans from model architecture to deployment and customer workflow impact, with a strong emphasis on shipping production-ready AI solutions. | AgentServe | 8 |
| Applied Scientist, FinTelligence This role focuses on building and scaling generative AI applications for finance teams, involving the development of autonomous agents, efficient inference systems, and robust evaluation frameworks. The position emphasizes shipping production-ready AI systems that handle sensitive financial data and require high precision and reliability. | AgentServe | 8 |
| Sr. Software Development Engineer, Products and Solutions Senior Software Development Engineer to own the architecture and delivery of production systems for AI-powered enterprise cloud migration products. The role involves building agentic AI solutions where AI agents and human consultants collaborate, focusing on orchestration, workflow, and platform infrastructure within the AWS ecosystem. | Agent | 8 |
| Software Development Engineer, Products and Solutions Software Development Engineer to build AI-powered agentic solutions for enterprise cloud migrations, acting as a platform where AI agents and human consultants collaborate. The role involves designing and delivering production features end-to-end within a product engineering team, focusing on agent logic, orchestration, and integration with AWS services. | Agent | 8 |
| Software Development Engineer, Products and Solutions Software Development Engineer to build AI-powered agentic solutions for enterprise cloud migrations, acting as a platform where AI agents and human consultants collaborate. The role involves designing and delivering production features end-to-end within a product engineering team, focusing on agent logic, orchestration, and integration with AWS services. | Agent | 8 |
| Software Development Manager, AWS Neuron SDK - Distributed Training Software Development Manager for AWS Neuron SDK, focusing on distributed training for ML accelerators. The role involves leading a team to design and deploy new products, optimize performance of ML models at scale, and ensure support for key ML functionality. Responsibilities include customer onboarding, maximizing model FLOPS utilization, building tooling, partnering with other teams, and driving technical strategy for frontier model architectures. | Post-trainServe | 8 |
| Applied Scientist, WHS Data-Tech Develop and deploy computer vision and machine learning models for workplace safety applications, impacting millions globally. Collaborate with engineers to productionize models and analyze data for safety risk identification. Focus on shipping models with measurable safety outcomes. | ShipServe | 8 |
| Principal UX Designer, Prime Video Experience Design (PVXD) This role is for a Principal UX Designer in an experimental lab focused on generative AI and conversational experiences for Prime Video. The designer will explore the frontier of AI in entertainment, creating experiences like AI characters and generative video, and will be hands-on with prototyping and emergent generative tools. The role requires a blend of moonshot thinking and practical craft, with a focus on designing human-like conversational AI and advocating for bold AI-driven product directions. | Agent | 8 |
| Applied Scientist II - GenAI/LLM, Translation Services Applied Scientist II role at Amazon focusing on designing and developing scalable machine learning solutions for language translation services using GenAI/LLMs. The role involves applying expertise in LLM models, collaborating with cross-functional teams, conducting data analysis, and evaluating state-of-the-art modeling techniques to improve translation accuracy and efficiency. The team has a startup mindset and aims to build scalable solutions from scratch. | Post-train | 8 |
| Applied Science Manager , C360 Manager for a team working on LLM and VLM post-training and alignment for personalized shopping experiences, leveraging customer behavioral data. | Post-trainAgent | 8 |
| Software Development Engineer, Sponsored Products and Brands Software Development Engineer role focused on building and shipping the SPB Agent platform, which powers agentic AI experiences for advertisers on Amazon. The role involves architecting and developing generative AI systems that reason over campaign performance, make autonomous decisions, and deliver personalized guidance at scale, integrating LLMs and distributed systems within the advertising domain. | Agent | 8 |
| Applied Scientist, Amazon Robotics, Compass Team Applied Scientist role focused on developing and deploying AI-driven manipulation algorithms for robots in unstructured environments, with a strong emphasis on safety, contact-rich tasks, and sim-to-real transfer. The role involves designing learning-based and model-based approaches, integrating with safety software, and deploying on physical hardware. | ShipData | 8 |
| Postdoctoral Researcher – Visual Localization & Navigation | Amazon Last Mile Research scientist role focused on visual localization and navigation for robotics and logistics platforms, involving metric-semantic mapping, relocalization, and monocular localization using geometric and learning-based approaches. | Agent | 8 |
| Applied Scientist, Prime Video Commerce Insights Applied Scientist role focused on Reinforcement Learning and ML for personalization in Prime Video Commerce, involving research, design, implementation, and deployment of recommendation systems at scale with low latency. The role aims to improve customer experience and business metrics by applying advanced ML techniques and contributing to the science roadmap. | AgentServe | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 8 |
| Applied Scientist II, Demand Enablement, Product Analytics and Operations This role focuses on designing and building intelligent multi-agent systems for automating root cause analysis in advertising campaign delivery at scale. It involves architecting agentic orchestration, developing hierarchical analysis frameworks, and creating self-learning feedback loops. The goal is to significantly reduce advertiser escalation time by leveraging LLM-based agent architectures and retrieval-augmented generation. | Agent | 8 |
| Solutions Architect III, HCLS AI/ML and Data Strategy Specialists, Global Healthcare Solutions Architect III for AWS Healthcare & Life Sciences, focusing on architecting production AI/ML systems, including generative AI and agentic architectures, for healthcare organizations. The role involves leading technical engagements, defining reference architectures, influencing stakeholders, and producing thought leadership content, with a strong emphasis on regulated environments. | AgentData | 8 |
| Public Sector Partner SA - OpenAI , WWPS Global ISV Partners This role focuses on being a technical partner lead for OpenAI within AWS's public sector, designing, prototyping, and deploying generative AI solutions. It requires hands-on engineering skills to build prototypes, evangelize capabilities, publish reference architectures, and work with both developers and executives in regulated and classified environments. The role involves deep collaboration with OpenAI and government customers, focusing on RAG, agentic workflows, fine-tuning, and multi-modal deployments on AWS. | AgentServe | 8 |
| Applied Scientist, Prime Video Commerce Insights Applied Scientist role focused on building and deploying ML-driven personalization and recommendation systems for Prime Video's commerce journey. The role involves researching, designing, and implementing models at scale, collaborating with engineers for production deployment, and contributing to the science roadmap with a focus on reinforcement learning and customer behavior. | AgentServe | 8 |
| Applied Scientist, Edge AI and Science Applied Scientist role focused on compressing generative AI models (LLMs, VLMs, speech, audio, omni) for edge and cloud deployment. The role involves applying and extending state-of-the-art compression techniques (knowledge distillation, pruning, quantization), designing healing recipes (fine-tuning) to recover accuracy, building reference implementations for partner teams, and defining benchmarks for evaluating trade-offs (accuracy, latency, memory, cost). The goal is to make training-to-deployment seamless. | ServePost-train | 8 |
| Sr. Applied Scientist, C360 Senior Applied Scientist role focused on advancing Information Retrieval, NLP, and Large Language Models for e-commerce personalization. The role involves post-training LLMs (instruction tuning, reward modeling, RL, multi-modal alignment), designing large-scale experiments, analyzing model behavior, and developing training recipes to improve capabilities like reasoning and personalization. It also includes owning the scientific roadmap, leading end-to-end systems, driving technical decisions, mentoring, and publishing research. | Post-trainAgent | 8 |
| Senior Applied Scientist, C360 Senior Applied Scientist role focused on improving shopping experiences using LLMs. The role involves post-training activities like instruction tuning, reward modeling, reinforcement learning, and aligning LLMs with embedding modalities. Responsibilities include designing and running large-scale experiments, analyzing model behavior, and developing new training recipes to enhance reasoning and personalization. | Post-trainPretrain | 8 |
