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 |
|---|---|---|
| Applied Science Manager, Alexa Edge AI Manager for a new Alexa Edge AI team in Bangalore, focused on developing and deploying on-device ML models for computer vision, acoustic modeling, and multimodal understanding to power Alexa devices. The role involves building and leading a team, driving R&D for privacy-preserving edge solutions, optimizing for resource-constrained hardware, and collaborating with hardware/silicon teams. Emphasis on end-to-end lifecycle ownership, from research to production deployment at scale, with a focus on latency, privacy, and accuracy. | ServePost-train | 9 |
| Senior Applied Scientist This role focuses on developing and deploying ML-based perception systems for robots using radar and thermal imaging, fusing this data with traditional sensors to enable operation in challenging conditions. The primary output is the deployed perception system (L3), with significant work also in developing and refining the ML models themselves (L2). |
| ServePost-train |
| 9 |
| Sr Applied Scientist, ML Codesign, Edge AI Platform This role focuses on the joint optimization of model compression and silicon architecture for Amazon's edge and cloud inference accelerators. The scientist will define the hardware-aware compression roadmap, own the optimization of compression algorithms with hardware, and represent applied science in silicon architecture reviews. The goal is to ship advanced quantization and distillation techniques in production for large language models. | ServePost-train | 8 |
| Senior Applied Scientist, Amazon Ads, Demand Tech , Amazon Advertising, Demand Tech Senior Applied Scientist role focused on building and improving deep learning models for response prediction and incrementality in Amazon's advertising platform. The role involves end-to-end ownership from design to production deployment, with a strong emphasis on low-latency, high-throughput inference and online A/B testing. Collaboration with engineers on serving infrastructure and mentoring junior scientists are also key aspects. | ServePost-train | 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 |
| 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, 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 |
| Senior Applied Scientist, Generative Artificial Intelligence (AI) Innovation Center This role focuses on researching, designing, and developing generative AI algorithms and ML techniques to solve real-world challenges for AWS customers. The scientist will collaborate with internal teams and directly with customers to understand business problems, implement AI solutions, and provide feedback to product and engineering teams. Key responsibilities include working with deep learning, deploying ML solutions, and understanding generative AI and foundational models. | Serve | 8 |
| Sr GenAI Infra Specialist SA, AWS WWSO Startup Senior GenAI Infrastructure Specialist SA for AWS WWSO Startup team, focusing on advising startup customers on AI infrastructure for model training and inference optimization. This role involves deep technical guidance on hardware, orchestration, frameworks, and optimization techniques for large-scale AI workloads on AWS. | ServePost-train | 8 |
| Senior Software Engineer (ML), Data Plane Senior Software Engineer focused on optimizing the ML inference data plane for custom hardware, involving compute kernels, serving integration, and end-to-end model execution for large distributed models. | Serve | 8 |
| Machine Learning Engineer, CreativeX Machine Learning Engineer to join the CreativeX RAPID team, focusing on Dynamic Creative Optimization (DCO). The role involves leveraging generative AI technologies like latent diffusion models, LLMs, RL, and computer vision to tailor ad experiences in real-time with low latency. Responsibilities include investigating new technologies, prototyping, evaluating feasibility, building data pipelines, and developing ML model deployment platforms. | ServePost-train | 8 |
| Software Development Engineer - AI/ML, Amazon Neuron, Multimodal Inference Software Development Engineer focused on optimizing and accelerating deep learning and GenAI workloads on AWS's custom ML accelerators (Inferentia and Trainium) through the AWS Neuron SDK. This role involves architecting, implementing, and tuning distributed inference solutions, focusing on performance optimization (latency and throughput) from system level to framework level (PyTorch, JAX). The engineer will work on low-level optimizations, system architecture, and ML model acceleration, collaborating across hardware, compiler, runtime, and framework teams. | Serve | 8 |
| Machine Learning SDE, Scanless Technologies Machine Learning Software Development Engineer focused on computer vision models for robotics applications within Amazon's fulfillment and delivery network. The role involves designing, building, and maintaining end-to-end ML solutions from data collection and training to deployment on edge devices, with a strong emphasis on operationalizing research models and ensuring model health in production. | ServePost-train | 8 |
| Sr. Machine Learning Compiler Engineer, AWS Neuron, Annapurna Labs This role focuses on developing and scaling a machine learning compiler for AWS Neuron, which optimizes the performance of neural network models on custom AWS hardware accelerators (Inferentia and Trainium). The engineer will architect and implement features for the compiler stack, which integrates with popular ML frameworks, aiming to improve inference and training performance for large ML workloads. | Serve | 8 |
| Senior ML Engineer, Fauna Senior ML Engineer to build and scale ML systems for intelligent robots, focusing on designing and maintaining infrastructure for training, evaluating, and deploying ML models. The role involves working at the intersection of ML and systems engineering to ensure robust, efficient, and scalable systems, with a focus on optimizing model inference for edge devices. | ServeData | 8 |
| Machine Learning Engineer, Alexa AI Machine Learning Engineer for Alexa AI focused on LLM training, production deployment, and inference optimizations. Will collaborate with Applied Scientists and other MLEs to leverage Amazon's data and computing resources for Generative AI solutions. Responsibilities include investigating design approaches, prototyping, evaluating technical feasibility, processing data, scaling ML models, and delivering high-quality software in an Agile environment. Experience with PyTorch/JAX, vLLM, SGLang, TensorRT, and developing large model hosting platforms is preferred. | ServePost-train | 8 |
| Senior Manager, AI Red Team, Threat Operations Senior Manager to lead an AI Red Team focused on security research and offensive operations targeting AI systems, infrastructure, and emerging threats. The role involves building and leading a team, establishing the AI offensive security research program, driving Red Team operations, and partnering with stakeholders to protect AI offerings and customer trust. | ServeData | 8 |
| ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs The role focuses on optimizing ML kernel performance for AWS Neuron SDK on custom ML accelerators (Inferentia and Trainium). It involves designing and implementing high-performance compute kernels, analyzing and optimizing kernel-level performance, implementing compiler optimizations, and collaborating with customers and internal teams to enable and optimize ML models. The work is at the hardware-software boundary, combining deep hardware knowledge with ML expertise. | Serve | 8 |
| Software Engineer II- AI/ML, AWS Neuron Software Engineer II role focused on optimizing and enabling deep learning and GenAI workloads on AWS custom ML accelerators (Inferentia and Trainium) by developing and enhancing the AWS Neuron SDK. This involves working across the stack from frameworks like PyTorch/JAX to hardware-software boundaries, optimizing ML compilers, runtimes, and high-performance kernels for inference and training. The role requires strong software development skills in Python/C++, system-level programming, ML knowledge, and collaboration with various teams to ensure optimal performance for customers. | ServePost-train | 8 |
| Software Development Engineer II, Items and Relationships Platform Software Development Engineer II role focused on building and optimizing GenAI serving systems and ML platforms at massive scale. The role involves working with LLMs, VLMs, and multimodal foundation models, including optimized model serving, distillation, quantization, distributed inference, vector indices, and agentic systems. The primary focus is on the engineering and infrastructure aspects of bringing AI models to production, with a secondary involvement in agentic systems. | ServeAgent | 8 |
| Senior Software Development Engineer - AI/ML, AWS Neuron, Multimodal Inference Senior Software Development Engineer for AWS Neuron, focusing on accelerating deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). The role involves designing, developing, and optimizing ML models and frameworks for deployment, with a strong emphasis on distributed inference, performance tuning (latency and throughput), and system-level optimizations for LLMs. | Serve | 8 |
| Senior Software Engineer, Speech MLOps Senior Software Engineer focused on MLOps for speech synthesis and GenAI experiences, involving building and maintaining ML infrastructure for the entire lifecycle on AWS. | ServePost-train | 8 |
| Compiler Engineer II - Machine Learning, Annapurna Labs The role involves developing and scaling a deep learning compiler stack for AWS Machine Learning accelerators (Inferentia and Trainium chips). The engineer will architect and implement features for the AWS Neuron SDK, focusing on making LLM and Vision models run performantly on accelerators. This includes compiler development, optimization, and integration with ML frameworks like PyTorch, TensorFlow, and JAX. | Serve | 8 |
| 2026 Annapurna Labs at AWS, Early Career (US) - Machine Learning Systems & Silicon Innovation This role focuses on building and optimizing the systems and silicon that power AI infrastructure, including custom ML accelerator chips, distributed training systems, and compiler optimizations for ML training. It's an early career role within Annapurna Labs at AWS, aiming to accelerate AI development. | Serve | 8 |
| Applied Scientist II, Campaign and Creative This role focuses on building and deploying machine learning models for computer vision systems on robotic platforms, specifically for automotive shopping experiences. It involves end-to-end solution delivery, from design and implementation to optimization and deployment on the edge, with a strong emphasis on deep learning and computer vision techniques. | ServePost-train | 8 |
| Senior Software Development Engineer - AI/ML, AWS Neuron Senior Software Development Engineer for AWS Neuron, focusing on accelerating deep learning and GenAI workloads on custom ML accelerators (Inferentia and Trainium). The role involves optimizing inference performance for LLMs, working across the stack from frameworks to hardware-software boundaries, and collaborating with compiler, runtime, and hardware teams. Key responsibilities include designing, developing, and optimizing ML models and frameworks, building infrastructure for model onboarding, implementing low-level optimizations, and working with customers on model enablement. | Serve | 8 |
| Software Development Engineer - AI/ML, AWS Neuron Software Development Engineer focused on optimizing and enabling deep learning and GenAI workloads, specifically LLMs, on AWS's custom ML accelerators (Neuron SDK, Inferentia, Trainium). The role involves system-level and low-level optimizations for inference performance, working across frameworks, kernels, and hardware boundaries. | Serve | 8 |
| Applied Scientist, AWS Neuron Science Team Applied Scientist role focused on enhancing AWS software stack for Trainium and Inferentia accelerators, involving ML/RL for kernel/code generation, ML compiler techniques, system robustness, and efficient kernel development. Collaborates with customers and engineering teams to optimize ML systems and adoption. | ServePost-train | 8 |
| Sr. AI Process Engineer, Seller Compliance Senior Process Engineer to lead AI-driven engineering initiatives in the Compliance domain, focusing on designing, building, and operating AI-powered solutions to improve Seller compliance outcomes and operational efficiency. Requires deep hands-on expertise in AI/ML development, building/deploying/scaling production AI systems, designing architectures, building ML models and data pipelines, and driving technical collaboration. Experience with Python, cloud platforms (AWS), and ML operations is essential. | Serve | 8 |
| Software Development Engineer, AI/ML, AWS Neuron, Model Inference Software Development Engineer focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium), working across the stack from frameworks like PyTorch/JAX to hardware-specific optimizations and kernel development. | Serve | 8 |
| Sr. Software Engineer- AI/ML, AWS Neuron Apps Senior Software Engineer role focused on optimizing and deploying large AI models (LLMs, vision generative AI) on AWS's custom AI accelerators (Inferentia, Trainium). The role involves architecting distributed inference solutions, optimizing performance from high-level frameworks to hardware implementations, and developing tools for LLM accuracy and efficiency. It bridges ML frameworks (PyTorch, JAX) with AI hardware, focusing on inference performance and scaling. | Serve | 8 |
| Senior Software Development Engineer, AI/ML, AWS Neuron, Model Inference Senior Software Development Engineer role focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium) through the Neuron SDK. The role involves system-level optimizations, performance tuning for latency and throughput, building infrastructure for model onboarding, and collaborating across hardware, software, and framework teams to ensure optimal performance for customers running large language models and other GenAI workloads. | Serve | 8 |
| Software Development Engineer AI/ML, Inference Serving, AWS Neuron Software Development Engineer to lead and architect next-generation model serving infrastructure for generative AI applications on AWS Inferentia and Trainium accelerators, focusing on performance, reliability, and scalability of inference serving systems. | Serve | 8 |
| Software Engineering Manager, ML Kernel Performance, AWS Neuron, Annapurna Labs The Annapurna Labs team at AWS is seeking an Engineering Manager to lead a team focused on optimizing ML kernel performance for AWS Neuron, their custom ML accelerators (Inferentia and Trainium). The role involves designing and implementing high-performance kernels, optimizing compiler and runtime performance, and working closely with customers to enable their ML models. This position operates at the hardware-software boundary, combining deep hardware knowledge with ML expertise to accelerate deep learning and GenAI workloads. | Serve | 8 |
| Software Development Engineer, AI/ML, AWS Neuron, Model Inference Software Development Engineer focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium), working across the stack from frameworks like PyTorch/JAX to hardware-specific optimizations and kernel development. | Serve | 8 |
| Software Engineer II - AI/ML, AWS Neuron, LLM Inference, AI/ML, AWS Neuron, Model Inference Software Engineer II role focused on optimizing LLM inference performance on AWS custom ML accelerators (Inferentia and Trainium) using the AWS Neuron SDK. This involves developing and tuning ML models and frameworks, building infrastructure for model onboarding, implementing low-level optimizations, and collaborating across hardware, software, and ML teams to ensure peak performance for customers. | Serve | 8 |
| Senior Software Development Engineer, AI/ML, AWS Neuron, Model Inference Senior Software Development Engineer role focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium). The role involves working across the stack from frameworks (PyTorch, JAX) to hardware, building infrastructure, optimizing performance (latency and throughput), and collaborating with various teams and customers to ensure efficient execution of large language models and other GenAI workloads. Experience with inference serving platforms like vLLM is required. | Serve | 8 |
| ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs The role focuses on optimizing ML kernel performance for AWS Neuron SDK on custom ML accelerators (Inferentia and Trainium). It involves designing and implementing high-performance compute kernels, analyzing and optimizing kernel-level performance, implementing compiler optimizations, and collaborating with customers and internal teams to enable and optimize ML models. The work is at the hardware-software boundary, combining deep hardware knowledge with ML expertise. | Serve | 8 |
| SDE II, ML Infra Services, Annapurna Labs Software Engineer to lead the development of machine learning tools to run, optimize, and analyze machine learning workloads on AWS Neuron ML accelerators. Focus on ML infrastructure platform, capacity management, workload scheduling, and fleet orchestration. | Serve | 7 |
| Software Development Engineer, Health & Wellness, Health Tech Software Development Engineer role focused on architecting and implementing ML systems for health initiatives, integrating genomic, proteomic, and clinical data. The role involves building high-throughput pipelines, low-latency inference services for biological foundation models, and productionizing ML models for tasks like neoantigen prediction, with a strong emphasis on collaboration with researchers and biologists in an early-stage environment. | ServePost-train | 7 |
| Principal Software Development Engineer, AWS Mantle Principal Software Development Engineer for AWS Mantle team, focusing on building and scaling a distributed inference engine for foundation models on Amazon Bedrock. The role involves defining technical vision, owning system design, influencing strategy, and ensuring high performance, reliability, and security for millions of customers. | Serve | 7 |
| Sr. Manager, Software Development, AWS Mantle Senior Manager, Software Development to lead multiple engineering teams building AWS Mantle, a next-generation distributed inference engine for Amazon Bedrock. The role involves owning technical and organizational strategy, building high-performing teams, and driving the delivery of globally distributed AI infrastructure. | 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 generative AI and large language models. The engineer will be responsible for designing, delivering, and optimizing server hardware and software systems to enable high performance and scalability for AI/ML workloads, with a focus on price-performance improvements. The role involves creating automation through agentic workflows and implementing AI-driven tools to enhance engineer productivity and influence AI implementation and core architecture. | Serve | 7 |
| Principal Software Development Engineer, AWS Mantle Principal Software Development Engineer for AWS Mantle team, focusing on the distributed inference engine that powers Amazon Bedrock. The role involves defining and executing technical vision for large-scale, ambiguous challenges at the intersection of ML systems, distributed computing, and security, shaping how foundation models are accessed globally. Key responsibilities include setting technical direction, owning system design, influencing engineering strategy, and mentoring senior engineers. | Serve | 7 |
| Software Development Engineer, Amazon Pharmacy, Amazon Phamarcy Software Development Engineer role at Amazon Pharmacy focusing on building ML-driven supply chain systems. Responsibilities include system design, development, operational ownership, and collaboration, with an emphasis on productionizing ML models for demand forecasting, procurement, and inventory placement. The role involves working with large-scale datasets, distributed systems, and operations research techniques within a regulated healthcare environment. | ServeData | 7 |
| Software Development Engineer, Amazon Pharmacy, Amazon Phamarcy Software Development Engineer role at Amazon Pharmacy focusing on building ML-driven supply chain systems. Responsibilities include system design, development, operational ownership, and collaboration, with an emphasis on productionizing ML models for demand forecasting, procurement, and inventory placement. The role involves working with large-scale datasets, distributed systems, and operations research techniques within a regulated healthcare environment. | ServeData | 7 |
| Software Development Engineer, WHS Data-Tech Software Development Engineer role focused on building and deploying AI and computer vision systems for workplace safety at Amazon. The role involves designing, developing, and maintaining production services, optimizing data pipelines, integrating ML model outputs, and collaborating with applied scientists to productionize models. It emphasizes end-to-end ownership from data ingestion to edge deployment and operational maintenance. | ServePost-train | 7 |
| Software Development Engineer, WHS Data-Tech Software Development Engineer to build AI and computer vision systems for workplace safety, focusing on real-time inference, ML model deployment at the edge, and integrating ML outputs into internal tools. The role involves end-to-end software development, from data ingestion to production services. | ServePost-train | 7 |
| Post-Silicon Systems Validation Engineer, Annapurna Labs This role focuses on validating next-generation machine learning accelerators for AWS cloud infrastructure. The engineer will be responsible for the complete vertical stack, from silicon to system-to-system interfaces, ensuring the quality and performance of AI/ML accelerators. This involves developing validation strategies, executing test plans, debugging hardware, and collaborating with various engineering teams throughout the product development lifecycle. | ServeAgent | 7 |
| Software Development Engineer, Alexa Audio Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences, aiming to improve model accuracy, reduce latency, and enhance customer features. The role involves designing, implementing, and delivering software, creating infrastructure for LLMs in audio, and influencing the operational roadmap for core audio services. | ServePost-train | 7 |