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 |
|---|---|---|
| Delivery Consultant - AI/ML, Professional Services - AWS Industries This role focuses on designing, implementing, and deploying GenAI and ML applications for customers on AWS. It involves providing technical guidance, gathering requirements, and supporting model training, building, and deployment strategies. The role requires experience with AWS AI/ML services and hosting/deploying ML solutions, with a preference for deep learning experience. | ServePost-train | 7 |
| Senior Delivery Consultant - AI/ML, AWS Professional Services Senior Delivery Consultant for AWS Professional Services focusing on designing, implementing, and managing AI/ML and Generative AI solutions for enterprise customers. This role involves leading project teams, hands-on development, MLOps, and acting as a trusted advisor on cloud AI architectures. |
| ServeAgent |
| 7 |
| ML Accelerator Performance Validation Engineer, Post Silicon Validation This role focuses on validating the performance of custom ML training chips for AWS, ensuring they meet latency, throughput, and efficiency targets at cloud scale. The engineer will design and execute performance benchmarks, profile ML workloads on silicon, identify bottlenecks, and build automated dashboards to track performance and readiness for production deployment. | Serve | 7 |
| Software Development Engineer, Item Identity Services (IIS) Software Development Engineer to help architect, build, and ship an AI-native matching platform that replaces legacy rule-based and classical ML matching algorithms with LLM-powered AI systems. The role involves serving LLM-based inference over petabyte-scale product data with sub-millisecond latency requirements, handling millions of requests per second, and solving novel computer science problems at the intersection of LLMs, retrieval, distributed systems, and large-scale data processing. | ServeAgent | 7 |
| Applied Scientist, AWS Neuron Science team Applied Scientist role focused on enhancing the AWS Neuron software stack for Trainium and Inferentia accelerators. The role involves working with customers to identify adoption barriers, collaborating with engineering teams on solutions, and engaging with research communities to advance ML systems. Key areas include AI for Systems (kernel/code generation, optimization), Machine Learning Compiler, System Robustness, and Efficient Kernel Development. | ServeData | 7 |
| Sr. Machine Learning - Compiler Engineer III, AWS Neuron, Annapurna Labs Senior Software Engineer role focused on building and optimizing the AWS Neuron compiler for AWS Inferentia and Trainium chips. The role involves transforming ML models (PyTorch, TensorFlow, JAX) into optimized implementations for deep learning workloads, with a focus on large language models and vision transformers. Responsibilities include compiler optimization, working with chip architects, and contributing to open-source communities. | Serve | 7 |
| Machine Learning Engineer II, Sponsored Products Search Sourcing, Amazon Advertising Machine Learning Engineer II at Amazon Advertising focused on building and optimizing ML systems for Sponsored Products and Brands search, handling billions of requests with low latency. The role involves developing scalable offline pipelines and online serving components, working with deep learning, NLP, and LLM training, and mentoring junior engineers. | ServePost-train | 7 |
| Sr. ASIC Engineer III, Annapurna Labs The Sr. ASIC Engineer III role at Amazon's Annapurna Labs focuses on the design and optimization of custom silicon (ASICs) and software that accelerate machine learning inference in AWS data centers, specifically for hardware like AWS Inferentia. This role is critical for enabling large-scale AI/ML workloads. | Serve | 7 |
| Sr. Software Development Engineer, Amazon Quick Senior Software Development Engineer role focused on building AWS AI services using ML and LLM technologies within the Information Retrieval space. The role involves designing, developing, testing, and deploying distributed ML systems and large-scale solutions, with a focus on inference and serving infrastructure for AI services. | Serve | 7 |
| Sr. Software Development Engineer, Amazon Quick Senior Software Development Engineer role focused on building AWS AI services using ML and LLM technologies within the Information Retrieval space. The role involves designing, developing, testing, and deploying distributed ML systems and large-scale solutions, with a focus on inference and serving infrastructure for AI services. | Serve | 7 |
| Sr. Software Development Engineer, Amazon Quick Senior Software Development Engineer role focused on building AWS AI services using ML and LLM technologies within the Information Retrieval space. The role involves designing, developing, testing, and deploying distributed ML systems and large-scale solutions, with a focus on inference and serving infrastructure for AI services. | Serve | 7 |
| Software Engineer II, Quick, Amazon Quick Software Development Engineer II role at Amazon, focusing on building AWS AI services that leverage ML and LLM technologies within the Information Retrieval space. The role involves designing, developing, testing, and deploying distributed ML systems and large-scale solutions, collaborating on strategy and roadmap, and driving system architecture. It emphasizes working with new ML technologies on a high-visibility product. | Serve | 7 |
| Software Engineer II, Quick, Amazon Quick Software Development Engineer II role at Amazon, focusing on building AWS AI services that leverage ML and LLM technologies within the Information Retrieval space. The role involves designing, developing, testing, and deploying distributed ML systems and large-scale solutions, collaborating on strategy and roadmap, and driving system architecture. It emphasizes working with new ML technologies on a high-visibility product. | Serve | 7 |
| ML Compiler Engineer, Annapurna Labs Seeking a skilled compiler engineer to develop and optimize a deep learning compiler stack for AWS ML accelerators (Inferentia/Trainium), supporting frameworks like PyTorch, TensorFlow, and JAX for domains including LLMs and Vision. Responsibilities include compiler feature design, optimization, and collaboration with hardware, runtime, and framework teams. | Serve | 7 |
| Sr. Software Development Manager - Performance and Tooling, AWS Neuron, Annapurna Labs This role is for a Sr. Software Development Manager leading a team of compiler engineers to develop, deploy, and scale a compiler targeting AWS Inferentia and Trainium chips. The focus is on optimizing performance, cost, and ease of use for the Neuron SDK, involving deep knowledge of resource management, scheduling, code generation, and optimization for various compute architectures. Experience with toolchains like LLVM/GCC and compiler internals is preferred. | Serve | 7 |
| Senior Software Engineer - AI/ML, AWS Neuron Inference Senior Software Engineer focused on optimizing LLM inference performance on AWS Neuron accelerators, working on core building blocks like Attention, MLP, Quantization, and Speculative Decoding. Collaborates with chip architects and compiler engineers to extract maximum performance and accuracy. | Serve | 7 |
| Sr. Delivery Consultant - AI/ML, AWS Professional Services Senior Delivery Consultant for AWS Professional Services, focusing on designing, implementing, and managing GenAI and ML solutions for customers. The role involves technical guidance, collaboration with stakeholders, and advising on AI/ML trends and technologies, with a strong emphasis on AWS services. | ServePost-train | 7 |
| Sr. Software Dev Engineer, Amazon Music Catalog Senior Software Development Engineer role focused on building and scaling GenAI and ML solutions within the Amazon Music Catalog team. The role involves developing, deploying, and optimizing LLMs in production, working with distributed systems and data pipelines, and partnering with Applied Scientists and Product Managers to integrate AI-driven capabilities into catalog systems. | ServePost-train | 7 |
| Software Development Engineer, CreativeX Software Development Engineer (MLE) for the CreativeX RAPID team at Amazon, focusing on real-time ad personalization and insights. The role involves tailoring visual ad experiences using technologies like latent diffusion models, LLMs, RL, and computer vision to provide dynamically optimized creatives with low latencies. Responsibilities include investigating new generative AI technologies, prototyping, evaluating feasibility, building data pipelines, and developing platforms for ML model deployment. | ServePost-train | 7 |
| Applied Scientist, Neuron ARG, Annapurna ML Applied Scientist role focused on the intersection of AI and program analysis for a deep learning compiler stack, optimizing models for ML accelerators like Trainium. Responsibilities include designing, developing, and deploying analyzers, architecting tooling, and publishing research in compiler technology and deep learning systems software. | Serve | 7 |
| Applied Scientist II - AMZ27057.1 The Applied Scientist II role at Amazon focuses on the design, development, evaluation, deployment, and updating of data-driven models and analytical solutions for ML and NL applications. The role involves applying statistical modeling, optimization, and ML techniques, routinely building and deploying ML models, and researching novel ML approaches. Mentoring junior scientists is also part of the responsibilities. The basic qualifications require a Master's degree or equivalent with one year of experience, or a Bachelor's degree with five years of experience, in a related field, with specific experience in programming (Java, C++, Python) and developing supervised/unsupervised ML models. | Serve | 7 |
| Machine Learning Engineer, Ad Response Prediction Machine Learning Engineer at Amazon Ads focused on Sponsored Products and Brands, re-imagining advertising with generative AI. The role involves designing, coding, and supporting scalable ML pipelines and online serving systems, optimizing ML model performance and infrastructure, and implementing end-to-end solutions. It requires driving technical direction, building and growing teams, and collaborating on product direction. The team operates on a large product catalog with strict latency constraints and works with research scientists to deliver relevant ads. | ServePost-train | 7 |
| Sr. Startup Solution Architect, GenAI , AWS Startups Frontier AI This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on providing architectural guidance, supporting the adoption of AWS services, and acting as a technical advisor to help startups build scalable, reliable, and secure solutions. | ServePost-train | 7 |
| Sr. Startup Solution Architect, GenAI , San Francisco GenAI Startups This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on architecting scalable, reliable, and secure solutions, supporting the adoption of AWS services, and providing feedback to product teams. The role also involves creating technical content and presenting at events. | ServePost-train | 7 |
| Sr. Delivery Consultant - AI/ML, AWS Professional Services This role is for a Senior Delivery Consultant focused on AI/ML solutions within AWS Professional Services. The consultant will design, implement, and manage AWS-based AI/ML applications and models for customers, providing technical guidance and support throughout the project lifecycle. Key responsibilities include gathering requirements, proposing model training and deployment strategies, and acting as a trusted advisor on AI/ML trends. The role requires experience with AWS AI/ML services and deploying LLMs in production. | ServePost-train | 7 |
| Sr. Startup Solution Architect, GenAI , San Francisco GenAI Startups This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on architecting scalable, reliable, and secure solutions, supporting the adoption of AWS services, and providing feedback to product teams. The role also involves creating technical content and presenting at events. | ServePost-train | 7 |
| Sr. Startup Solution Architect, GenAI , AWS Startups Frontier AI This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on providing architectural guidance, supporting the adoption of AWS services, and acting as a technical advisor to help startups build scalable, reliable, and secure solutions. | ServePost-train | 7 |
| Sr. Machine Learning - Compiler Engineer III, AWS Neuron, Annapurna Labs Senior Software Engineer role focused on building and optimizing the AWS Neuron compiler for AWS Inferentia and Trainium chips. The role involves transforming ML models (PyTorch, TensorFlow, JAX) into optimized implementations for deep learning workloads, with a focus on large language models and vision transformers. Responsibilities include compiler optimization, working with chip architects, and contributing to open-source communities. | Serve | 7 |
| Systems Development Engineer (AWS Generative AI & ML Servers), AWS HW Engineering This role focuses on building and operating AWS cloud infrastructure specifically for AI training and inference workloads. The Systems Development Engineer will design, deliver, and operate server solutions that enable high performance and scalability for AI/ML and HPC. The role involves creating automation through agentic workflows and implementing AI-driven tools, impacting both AI implementation and core architecture within the AWS Hardware Engineering team. | Serve | 7 |
| Senior Software Development Engineer Senior Software Development Engineer role focused on developing advanced robotics systems at Amazon scale, integrating cutting-edge AI, control systems, and mechanical design for adaptable automation solutions. The role involves leading a team to design and implement a software framework for generalized dexterous mobile manipulation, including cloud and on-edge inferencing, real-time teleoperation, and human-machine interfaces. | ServeAgent | 7 |
| (Fall 2026) Annapurna Labs at AWS Internship (US) - Machine Learning Systems & Silicon Innovation Internship role focused on building AI infrastructure and ML systems, including custom silicon design, framework optimization, compiler development, and distributed training systems for AWS. | Serve | 7 |
| Software Development Engineer, CreativeX Software Development Engineer focused on real-time ad personalization and insights, leveraging generative AI technologies like latent diffusion models, LLMs, and RL to tailor visual ad experiences. The role involves investigating new technologies, building data pipelines, and developing platforms for ML model deployment in the Dynamic Creative Optimization (DCO) domain. | ServePost-train | 7 |
| Software Dev Engineer, EC2 Nitro Software Development Engineer on the EC2 Nitro Machine Learning Systems team, focusing on building and optimizing performance measurement infrastructure for AI/ML workloads. The role involves establishing best-known configurations, translating performance insights into technical requirements, and analyzing training/inference performance across accelerated platforms. It requires expertise in low-level systems, ML frameworks, and serving layers. | Serve | 7 |
| Senior Software Development Engineer, EC2 Nitro Senior Software Development Engineer role focused on building and scaling EC2 compute platforms for machine learning workloads, including training and inference for LLMs and multimodal systems. The role involves designing innovative technologies, leading technical projects, developing regression testing systems, and collaborating with hardware teams to optimize platform designs for ML performance. | ServeAgent | 7 |
| Applied Scientist II, Annapurna ML Applied Scientist II role focused on enhancing ML accelerator software (Trainium/Inferentia) to accelerate customer adoption. Responsibilities include developing ML/RL for code generation/optimization, creating ML compiler techniques, building validation tools, and designing high-performance kernels. The role involves working with customers, engineering teams, and research communities to advance ML systems, with a focus on inference performance and training cost optimization. | ServeData | 7 |
| Interdisciplinary Sys Engineer, GES NA Ops Engineering This role focuses on integrating computer vision, edge computing, and physical automation systems to enable real-time operational intelligence, improve equipment performance, and optimize process flow within global fulfillment networks. The engineer will bridge AI/ML models with physical systems, leading the development and deployment of sensor-driven automation solutions and ensuring seamless integration across hardware, software, and control layers. | ServeAgent | 7 |
| Senior Applied Scientist, Agentic WorkSpaces Senior Applied Scientist role focused on building predictive intelligence for capacity management in AWS workspaces. This involves developing ML systems for demand forecasting, resource optimization, and cost efficiency at enterprise scale. The role requires translating business needs into production ML systems, designing algorithms, and applying advanced ML techniques like time-series forecasting, reinforcement learning, and causal inference. Emphasis on low-latency, large-scale data processing, and collaboration with product and engineering teams. | ServeAgent | 7 |
| Sr. Worldwide Specialist - GenAI, Foundation Models, Data & AI GTM This role focuses on defining and executing Go-to-Market (GTM) strategies for AWS's generative AI (GenAI) infrastructure, specifically targeting large-scale model training and inference workloads. The individual will work with key customers (Frontier AI model builders) to accelerate their adoption of AWS services, understand their infrastructure needs, and influence product roadmaps. The role involves business development, customer engagement, evangelism, and collaboration with internal AWS teams. | ServePretrain | 7 |
| Software Dev Engineer, AWS Identity Analytics Platform Software Development Engineer role focused on building and operating the data platform infrastructure for an AI-driven analytics platform at AWS Identity. This involves designing and managing ingestion, transformation, and serving pipelines for petabyte-scale data to feed ML models and LLM agents. The role also includes productionizing ML models, building feature engineering infrastructure, and ensuring platform resilience and scalability. | ServeData | 7 |
| Software Development Engineer, Data Integration AI and Platform Excellence (APEX) Software Development Engineer role focused on building AI/ML-powered products and infrastructure for data integration workflows at Amazon. | Serve | 7 |
| Software Dev Engineer, EC2 Nitro Software Development Engineer to build and optimize performance measurement infrastructure for AI/ML workloads on AWS EC2 Nitro. The role involves low-level systems, ML frameworks, and serving layers to translate performance insights into technical requirements for platform designs. | Serve | 7 |
| Senior Software Dev Engineer, EC2 Nitro Senior Software Development Engineer to build and optimize infrastructure for AI/ML workloads on EC2 Nitro. Focus on performance measurement, benchmarking, regression testing, and influencing future hardware designs for LLMs, multimodal systems, and emerging architectures. Role involves both customer-facing performance problem-solving and foundational infrastructure development. | Serve | 7 |
| Software Development Engineer II, Post Silicon Validation Software Development Engineer II, Post Silicon Validation for AWS's next-generation machine learning accelerators. This role involves validating the complete vertical stack of ML accelerators, from silicon to system, ensuring quality and performance for AWS cloud infrastructure. Responsibilities include developing validation strategies, executing test plans, hardware bring-up and debug, and collaborating with cross-functional teams. | Serve | 7 |
| Sr. Systems Development Engineer (AWS Generative AI & ML Servers), AWS HW Engineering This role focuses on building and operating AWS cloud infrastructure for AI training and inference, specifically targeting high-performance and scalable solutions for large language models. The engineer will work on server designs, system-level debugging, and implementing automation solutions, including agentic workflows and AI-driven tools, to enhance the productivity of other engineers and influence AI implementation and core architecture. | Serve | 7 |
| Machine Learning Engineer II, Special Projects Machine Learning Engineer II on an Amazon Special Projects team focused on creating new products and services using Generative AI and LLMs. Responsibilities include developing and maintaining platforms for LLM development, evaluation, and deployment, processing large datasets, scaling models, and optimizing performance. Experience with distributed model training is required. | ServePost-train | 7 |
| Software Engineer- AI/ML, AWS Neuron Distributed Training - Performance Optimization Software Engineer focused on performance optimization for distributed training of large-scale AI/ML models (LLMs, multi-modal) on AWS Neuron accelerators. This involves tuning across the software stack, including collective communications, memory utilization, compiler optimizations, and kernel performance, working with PyTorch and JAX. | ServePost-train | 7 |
| Software Development Manager - Compiler, AWS Neuron, Annapurna Labs Seeking a Software Engineering Manager to lead a team developing compiler optimization algorithms and deploying a new compiler for AWS custom hardware (Inferentia and Trainium chips). The role involves technical leadership, mentoring, and partnering with AWS ML services teams to improve deep learning model performance and productivity. | Serve | 7 |
| Software Development Engineer II, AI/ML Elastic Collectives - Annapurna Labs Software Development Engineer II at Amazon's Annapurna Labs, focusing on distributed AI/ML systems and collective operations for scaling AI across multiple accelerators and servers. The role requires strong C/C++ and Linux skills, with experience in embedded systems, high-speed networking, or HPC interconnects being valuable. This position is on the forefront of AI/ML, working with large-scale clusters and models within AWS's EC2 infrastructure. | Serve | 7 |
| Sr. Machine Learning - Compiler Engineer III, AWS Neuron, Annapurna Labs This role is for a Sr. Machine Learning Compiler Engineer III on the AWS Neuron team, focusing on the development and scaling of a compiler for ML accelerators. The role involves architecting and implementing features for a deep learning compiler stack that optimizes neural network performance on custom AWS hardware, integrating with frameworks like PyTorch and TensorFlow. The goal is to provide significant performance improvements for large-scale ML workloads. | Serve | 7 |
| Software Development Manager, Neuron Tools, Annapurna Labs Software Development Manager for AWS Neuron Tools team, responsible for leading engineers to develop and maintain high-performance monitoring and profiling tools for AI accelerators (Inferentia, Trainium). The role involves managing the full development lifecycle of the Neuron Profiler, ensuring scalability, reliability, and usability, and collaborating with cross-functional teams to optimize AI workloads. Experience with ML-specific profiler tools and performance analysis is required. | Serve | 7 |