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.
Currently tracking 1110 active AI roles, down 16% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$465k (avg $194k).
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 |
|---|---|---|
| Applied Scientist II, Financial Insights and Actions Applied Scientist II role focused on leveraging GenAI/LLMs to build agentic solutions for financial insights and actions within Amazon. The role involves developing AI trust and safety in the financial domain, creating training/evaluation datasets for fine-tuning, and collaborating with engineers for productionalization. It balances scientific research with production deployment, with opportunities for external publications. | AgentPost-train | 7 |
| Software Development Engineer, Alexa Connections Software Development Engineer role focused on building agentic APIs and integrating advanced AI technologies (Generative, Agentic, Real-time AI) into Alexa's communication features. The role involves influencing product strategy, optimizing for low latency and scalability, and ensuring high-quality software delivery within an Agile environment. |
| Agent |
| 7 |
| Sr. Software Development Engineer, Alexa Connections Senior Software Development Engineer role focused on building agentic APIs and cloud services for Alexa Connections, leveraging Generative, Agentic, and Real-time AI to enhance conversational experiences. The role involves influencing product strategy, optimizing software for performance, and leading integration of AI systems. | Agent | 7 |
| Software Development Engineer, Alexa Connections Software Development Engineer focused on building and scaling real-time agentic AI systems for Alexa's communication features, involving design, architecture, and full-stack development. | Agent | 7 |
| Software Development Engineer, AFT Quality Software Development Engineer role focused on building and deploying computer vision and machine learning systems for Amazon's global fulfillment network. The role involves end-to-end ownership of systems that provide vision-based insights for item identification, defect detection, and decision-making in manual and robotic operations, supporting downstream business processes and flagship devices. | ShipAgent | 7 |
| Software Dev Engineer II, AWS Elemental Inference Software Development Engineer II on the AWS Elemental Inference team, focused on building and shipping production code for AI-driven video processing systems. The role involves translating research into scalable services, improving performance, and implementing MLOps best practices for AI-enhanced systems. | ServeData | 7 |
| Sr. Data Scientist, Alexa Connections This role focuses on developing and deploying machine learning models for communication experiences within Alexa. Responsibilities include end-to-end model development, experimentation, and leveraging LLMs to build applications, aiming to improve customer engagement and product performance. | Agent | 7 |
| Software Development Engineer, Alexa Connections Software Development Engineer role focused on building agentic APIs and integrating advanced AI systems (Generative, Agentic, Real-time AI) into Alexa's communication features, aiming for low latency, high reliability, and scalability. | Agent | 7 |
| Software Development Engineer, Alexa Connections Software Development Engineer role focused on building agentic APIs and integrating advanced AI technologies (Generative, Agentic, Real-time AI) into Alexa's communication features. The role involves influencing product strategy, optimizing for low latency and scalability, and ensuring high-quality software delivery within an Agile environment. | Agent | 7 |
| Applied Scientist, Sales AI Applied Scientist role focused on building and refining Generative AI and ML models to optimize Amazon's Ad Sales business. The role involves conceptualizing research, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. The goal is to transform account team operations with actionable insights, recommendations, and GenAI integration for improved efficiency. | Ship | 7 |
| Applied Scientist, Sales AI Applied Scientist role focused on building AI/ML solutions for Amazon's Advertising Sales business. The role involves developing and implementing models for insights, recommendations, and generative AI-powered workflows to improve sales team efficiency and customer success. It requires expertise in quantitative modeling, deep learning, RL, and NLP, with a focus on production deployment and A/B experimentation. | AgentPost-train | 7 |
| Applied Scientist, Sales AI This role focuses on building AI agents to optimize end-to-end workflows for Amazon's Advertising Sales organization. It involves applying expertise in NLP, LLMs, Deep Learning, Reinforcement Learning, and Recommender Systems to create and refine production-ready models, with a strong emphasis on autonomous agents operating at scale. The scientist will also collaborate with engineering and product teams, conduct A/B experiments, and contribute to scientific publications. | Agent | 7 |
| Software Development Engineer, Middle Mile Disruption Detection and Execution Software Development Engineer role focused on building and deploying ML and GenAI models to resolve disruptions in Amazon's Middle Mile Transportation network. The role involves developing intelligent systems for predictive analytics and automation, operating high-volume, low-latency services, and collaborating with stakeholders. | Agent | 7 |
| Software Development Engineer, Middle Mile Disruption Management Software Development Engineer role focused on building intelligent systems powered by ML and GenAI to resolve transportation disruptions. The role involves building and deploying ML models for predictive analytics and leveraging generative AI for automation and decision support within Amazon's Middle Mile Transportation Technology team. | Agent | 7 |
| Applied Scientist, Sales AI Applied Scientist role focused on Generative AI and quantitative modeling for Amazon Advertising Sales. The role involves conceptualizing and leading research on ML/GenAI solutions, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. Key areas include optimizing sales business, improving work efficiency through GenAI, and developing advertiser insights and recommendations. | Ship | 7 |
| Sr. Software Development Manager - Compiler, AWS Neuron, Annapurna Labs The Sr. Software Development Manager will lead a team of compiler engineers developing, deploying, and scaling a compiler targeting AWS Inferentia and Trainium ML accelerators. This role involves deep knowledge of resource management, scheduling, code generation, and optimization for new instruction architectures, with a focus on delivering high-performance, low-cost ML inference and training solutions for AWS customers. | Serve | 7 |
| ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads. | Serve | 7 |
| Data Scientist II, Amazon Private Brands Data Scientist II at Amazon Private Brands focused on applying Generative AI, Machine Learning, Statistics, and Economics to product assortment, business decisions, and product inputs. The role involves investigating business problems, inventing novel solutions, prototyping, and deploying production software, with research areas including NER, product substitutes, pricing optimization, agentic AI, and LLMs. The Data Scientist will also guide other scientists and publish research. | Agent | 7 |
| Applied Scientist, Private Brands Discovery Applied Scientist role focused on designing and building machine learning solutions for customer discovery of Amazon's Private Brands. The role involves end-to-end project management from ideation to launch, with a strong emphasis on causal ML, deep learning, and deploying models to production. The goal is to drive customer awareness and product discovery, impacting Amazon's own brands and contributing to broader discovery solutions across the company. | Ship | 7 |
| ML Compiler Engineer , AWS Neuron, Annapurna Labs The AWS Neuron team is seeking ML Compiler Engineers to optimize deep learning and GenAI workloads on AWS custom ML accelerators (Inferentia/Trainium). This role involves analyzing and optimizing system-level performance across the entire technology stack, from frameworks to runtime, and designing/implementing compiler optimizations. The position requires a passion for performance analysis, distributed systems, and machine learning, with a focus on improving the performance capabilities of the AWS Neuron SDK. | Serve | 7 |
| Senior ML Kernel Performance Engineer The Annapurna Labs team at Amazon is seeking a Senior ML Kernel Performance Engineer to optimize deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). This role involves crafting high-performance kernels, pushing the boundaries of AI acceleration at the hardware-software boundary, and collaborating with customers to enable their models. The engineer will work on compiler optimizations, performance analysis, and contribute to future architecture designs. | Serve | 7 |
| Software Development Manager - ML Performance Tooling and Benchmarking, AWS Neuron, Annapurna Labs Manager III leading a team of compiler engineers to develop, deploy, and scale a compiler targeting AWS Inferentia and Trainium ML accelerators. The role involves technical leadership, innovation, and collaboration with AWS ML services teams to ensure the Neuron SDK meets customer needs for high performance, low cost, and ease of use. Deep knowledge of resource management, scheduling, code generation, and optimization is required. | Serve | 7 |
| ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads. | Serve | 7 |