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 |
|---|---|---|
| Senior Software Development Engineer - Generative AI, Neuron SDK Senior Software Development Engineer focused on Generative AI within Amazon's Annapurna Labs, specifically working with the Neuron SDK and ML chips (Inferentia and Trainium). The role involves building and applying AI agents to improve customer adoption of these chips, optimizing software solutions for performance, durability, cost, and security, and collaborating with cross-functional teams including compiler, hardware, and ML engineers. Experience in the Generative AI space is a hard requirement. | Serve | 7 |
| Data Scientist, SCOT Forecasting and Labs - CIV Team Data Scientist role focused on developing and implementing statistical, causal, and machine learning techniques for forecasting and inventory management within Amazon's retail supply chain. The role involves creating prototypes, collaborating with software teams for production implementation, and analyzing key business metrics to influence business direction. |
| 7 |
| Sr. Applied Scientist, Amazon Advertising Senior Applied Scientist role at Amazon Advertising focused on building and deploying end-to-end machine learning models to improve traffic monetization and merchandise sales. The role involves leading ML efforts, performing hands-on analysis, driving ambiguous projects, and establishing scalable processes for model development and deployment. | Ship | 7 |
| Software Development Engineer, JWO Software Development Engineer role on the AWS Solutions team, focusing on building and scaling the Machine Learning platform for Just Walk Out (JWO) Technology. The role involves developing algorithms for computer vision, image recognition, and machine learning within a distributed systems environment, with a focus on scaling ML platforms. | Serve | 7 |
| Sr. SDM, AI Inference Technology, Neuron SDK Senior Manager for AI Inference Technology, leading a team to build fundamental inference technology building blocks and libraries for AWS Neuron SDK, optimizing models for Trainium and Inferentia devices. Focuses on the full development life cycle of inference libraries, enabling customers to optimize LLMs, multimodal, and generative models. | 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 |
| Senior Applied Scientist, Prime Video: Playback Intelligence Senior Applied Scientist role at Amazon Prime Video focusing on Playback Intelligence. The role involves applying machine learning and data science to optimize video streaming quality, detect anomalies, and leverage LLMs and generative AI. Responsibilities include end-to-end ownership of product and user experience, translating business requirements into ML deliverables, defining research directions, conducting experiments, and mentoring junior scientists. The role requires experience in building ML models for business applications and designing AI solutions for real-world use cases. | ShipPost-train | 7 |
| Sr. SDE, MLA hardware/software co-design, Annapurna Labs Machine Learning Acceleration Senior Software Development Engineer focused on pre-silicon hardware/software co-development for next-generation machine learning chips (like Trainium) used in AWS. The role involves working with architecture, design, and emulation teams, writing bare-metal software and ML workloads to verify chip functionality and performance. | Data | 7 |
| Principal Applied Scientist, Amazon Stores Economics & Science (SEAS) Principal Applied Scientist role focused on applying machine learning, optimization, and economics to improve Amazon's Stores business, specifically in areas like delivery speed, seller fees, and LLM applications. The role involves leading a team, developing scientific models, benchmarks, and services, and deploying solutions in partnership with product teams. | Ship | 7 |
