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 |
|---|---|---|
| Principal Applied Scientist, Prime Video Personalization & Discovery Principal Applied Scientist role at Prime Video focused on inventing, developing, and deploying AI solutions for personalization and discovery. The role involves technical and strategic leadership, guiding ML systems from research to production, and mentoring scientists. Key responsibilities include prototyping and productionizing large-scale AI solutions using deep learning, generative AI, RL, and optimization, providing technical leadership, designing A/B tests, driving technical bar-raising, and staying ahead of industry trends. The team focuses on creating a highly personalized content discovery experience using ML and Generative AI. | ShipPost-train | 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 |
| Principal GenAI Specialist SA This role is for a Principal GenAI Specialist SA at Amazon, focusing on designing and architecting scalable, secure, and cost-effective AI/ML, Generative AI, and Agentic AI solutions on AWS. The role involves guiding customers through their AI transformation, establishing GenAIOps practices, and creating enterprise-grade AI architectures. It requires deep technical experience across the AI spectrum, including LLM customization/fine-tuning, inference optimization, agentic frameworks, GenAIOps, security, RAG systems, and prompt engineering. | Agent | 8 |
| Applied Scientist, Brand Protection Machine Learning Applied Scientist role focused on building and deploying Generative AI solutions for Brand Protection using NLP, computer vision, and LLMs. The role involves end-to-end ownership from conception to launch, collaborating with product and engineering teams, and analyzing data to solve complex business problems at scale. | Ship | 8 |
| Applied Scientist Applied Scientist role focused on developing and deploying production-ready AI/ML models for consumer-facing features like content understanding, recommendations, and GenAI applications. The role involves inventing new approaches, adapting existing ones, and building scalable, efficient solutions. It requires collaboration with scientists and engineers, with a focus on both scientific and engineering best practices, and potentially contributing to research papers. The role touches on inference infrastructure and model serving, with a primary focus on building agentic or product-level AI features. | AgentServe | 8 |
| Applied Scientist II, Console Science The Applied Scientist II will focus on building industry-leading Conversational AI Systems using Generative AI, LLMs, NLU, and Applied ML. The role involves developing novel algorithms and modeling techniques to advance human language technology, impacting millions of customers through products and services. The team explores new technologies and finds creative solutions for AWS customers, working with foundation models and generative AI to reimagine customer experiences. | AgentPost-train | 8 |
| Machine Learning Scientist - GenAI, KIT Machine Learning Scientist role focused on Generative AI within AWS, aiming to identify customer needs and improve cloud adoption. The role involves building Agentic AI systems, fine-tuning LLMs, applying Reinforcement Learning, and generating insights from large datasets, with a focus on taking ideas from conception to production. | AgentPost-train | 8 |
| Data Scientist, SPX AI Lab, SPX Science Data Scientist to build and launch production-grade agentic capabilities for Amazon Seller Assistant, a multi-agent GenAI system. Responsibilities include analyzing seller pain points, designing measurement frameworks, applying NLP and statistical modeling, and collaborating with cross-functional teams to improve the seller experience at Amazon's scale. | Agent | 8 |
| Senior Software Development Engineer , Stores Foundational AI - Rufus Senior Software Development Engineer focused on building and scaling foundational LLMs for Amazon Stores. The role involves architecting and building ML infrastructure for LLM training and post-training workflows (fine-tuning, RL, continuous learning), transforming customer interactions into training signals, optimizing RL systems, and partnering with scientists to productionize frontier techniques like RLHF and agentic workflows. Emphasis on end-to-end system ownership, including design, implementation, deployment, and observability, with a focus on low-level optimization like CUDA kernels and ML platforms. | Post-trainServe | 8 |
| Sr. Applied Scientist, Amazon Robotics, Structured Field Coordinated Planning & Control Senior Applied Scientist role focused on AI-driven structured field robotics, including path planning, fleet coordination, and control systems. The role involves leading research, translating breakthroughs into production solutions at scale, and owning end-to-end delivery of algorithmic solutions. It requires a PhD or Master's with significant experience in robotics, ML, and algorithm development, with a focus on publishing research and mentoring junior scientists. The team operates at the intersection of planning, algorithmic, and ML research with production systems. | AgentServe | 8 |
| Applied Scientist II, Foundation Model, Industrial Robotics Group The Applied Scientist II role focuses on developing and improving machine learning systems for industrial robotics, specifically leveraging and adapting foundation models for tasks like perception, reasoning, and action. This involves fine-tuning, optimization, experimentation, and building evaluation frameworks, with a contribution to data and training workflows. The goal is to enable generalization, multi-modal learning, and skill acquisition in robots operating at Amazon's scale. | AgentData | 8 |
| Applied Scientist, End User Messaging, AWS Applied AI Solutions Core Services This role focuses on developing advanced machine learning approaches and agentic systems for trust and safety in AWS cloud communication services. The primary goal is to create behavioral detection models and intelligent resource allocation algorithms that adapt to evolving threats and optimize service delivery. The role involves researching novel AI agent applications in security, integrating science components into production, and conducting rigorous experimentation. | Agent | 8 |
| Applied Scientist, Geospatial & Safety Science Applied Scientist role focused on leveraging computer vision, generative AI, and deep learning to enhance vehicle navigation and ensure safe, efficient deliveries by analyzing multimodal data. The role involves building large-scale ML systems, translating business requirements into prototypes, and optimizing models for production and edge devices. | ShipPost-train | 8 |
| Senior Applied Scientist, Special Projects Senior Applied Scientist role focused on building state-of-the-art ML models for healthcare challenges within Amazon's Special Projects team. The role emphasizes practical implementation, driving ML advancements, and delivering products to market in an entrepreneurial, startup-like environment. Requires a strong background in AI/ML, leadership skills, and the ability to translate research into actionable plans and practical solutions. | Ship | 8 |
| Applied Scientist II, Kiro Science Applied Scientist II role focused on building AI-based services for Amazon Q Developer, aiming to redefine developer workflows. The role involves working on ambiguous problem areas, driving the delivery of end-to-end modeling solutions, and collaborating with other AWS AI services. The team builds AI products deployed in IDEs, AWS console, and web tools, providing developers with AI assistants for code generation and AWS interaction. | Ship | 8 |
| Neuron Collectives Software Engineer, Trainium Collectives Software Engineer role focused on enhancing collective algorithms and topologies for optimal AI training performance on Amazon's Trainium chips. This involves optimizing communication primitives to scale AI compute across data centers, working closely with hardware teams, and developing C/C++ implementations for training LLMs. | Data | 8 |
| Principal Applied Scientist, AWS Marketplace & Partner Services Principal Applied Scientist at AWS Marketplace & Partner Services focused on developing and evaluating next-generation search, recommendation, and agentic systems to drive AWS revenue growth. The role involves defining technical strategy, leading innovations in information retrieval, recommendation systems, LLMs, and agentic AI, and mentoring other scientists. Key responsibilities include architecting agentic AI systems, bridging theory with practice, and contributing to the scientific community. | AgentServe | 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 |
| Sr Software Development Engineer , AXU Senior Software Development Engineer focused on building and architecting sophisticated AI agent systems leveraging LLM/SLM technologies, Amazon Bedrock's agent core, and custom MCP servers. The role involves creating intelligent automation, deploying ML products, advanced prompt engineering, and integrating agent frameworks to push the boundaries of generative AI for inclusive customer experiences. | AgentServe | 8 |
| Senior Software Development Engineer, GenAI, Ads Agentic Intelligence Senior Software Engineer to lead technical vision and innovation for a new team building a horizontal agentic AI layer for Amazon Advertising. The role involves architecting and implementing robust systems using LLMs and autonomous agents to transform advertiser interactions with the platform. | Agent | 8 |
| Machine Learning Engineer II , AGI Customization Machine Learning Engineer II on the AGI Customization team at Amazon, focusing on developing and optimizing LLM training techniques, including fine-tuning, distillation, model evaluation, and prompt optimization for multimodal LLMs and Generative AI solutions. | Post-trainData | 8 |
| Software Development Engineer (ML), AGI Customization, AGI Customization ML Engineer role focused on developing customization capabilities like fine-tuning and distillation for LLMs, advancing LLM training techniques, and optimizing multimodal LLMs and Generative AI solutions. Requires experience deploying LLMs in production and knowledge of ML frameworks. | Post-trainServe | 8 |
| Software Development Engineer III, Annapurna Labs Software Development Engineer III at Amazon Annapurna Labs, focused on building AI agents and tools to simplify and accelerate customer adoption of AWS Neuron, the software stack for Amazon's ML silicon (Trainium). The role involves technical leadership, research, and delivery of innovative software solutions to improve ML workload porting and optimization on AWS hardware. | Agent | 8 |
| Applied Scientist II - Gen AI & LLM, PXT Applied Scientist II role focused on designing, developing, and deploying Generative AI and LLM solutions for Amazon. The role involves working with foundation models, prompt engineering, RAG, fine-tuning, and production deployment of AI systems, with a focus on applied research and evaluation. | AgentPost-train | 8 |
| Member of Technical Staff, AGI Autonomy This role focuses on developing training environments, tasks, and integrations for scaling RL environments and core model capabilities for browser-based agents. The primary responsibility is to architect and deliver robust software solutions, including agentic harnesses, and engineer high-performance systems using TypeScript and Python. | AgentData | 8 |
| Sr. Machine Learning Engineer, AWS Applied AI Solution Senior Machine Learning Engineer at AWS Applied AI Solutions focused on building a new agentic product. The role involves transforming research into production systems, owning end-to-end deployment of Generative AI and ML methods, and establishing scalable processes for model development, validation, and serving. Requires expertise in agentic systems, production ML, and scalable deployment architectures, bridging research and customer-facing products. | AgentServe | 8 |
| Sr. Applied Scientist, Amazon Ads Senior Applied Scientist at Amazon Ads focusing on applying cutting-edge generative AI and LLMs to the advertising life cycle. The role involves researching, developing, and deploying ML solutions for ranking, personalization, NLP, computer vision, recommender systems, and LLMs. It requires driving end-to-end projects, building and optimizing models, running A/B experiments, and developing scalable ML processes. The role emphasizes impacting millions of customers and advertisers through innovative ML solutions at massive scale. | ShipServe | 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 |
| 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. Manager, Applied Science, Sponsored Products and Brands Senior Manager role leading a team of Applied Scientists and Engineers to develop and deploy generative AI solutions for Amazon's Sponsored Products and Brands advertising platform, focusing on multi-lingual and multi-modal applications to drive growth in non-US markets. | Ship | 8 |
| SDE- ML Engineer, Frontier AI Robotics Machine Learning Systems Engineer for Frontier AI Robotics team, focusing on building and optimizing distributed training infrastructure for large-scale deep learning and transformer models. Role involves engineering scalable, high-performance systems for AI research and applications, with a focus on robotics, multimodal perception, and manipulation strategies. Requires strong software development, ML infrastructure, and deep learning framework expertise. | Data | 8 |
| Senior Applied Science Manager, Amazon Sponsored Products & Brands Lead a team to invent and build the SPB-Agent, a GenAI platform transforming retail-media advertising for Amazon advertisers. This agent will act as an intelligent advisor integrated into Amazon Ad Console and Seller/Vendor portals, using conversational interfaces and deep reasoning to help advertisers discover growth opportunities, optimize campaigns, and execute strategies at scale. | Agent | 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 |
| Principal Applied Scientist, Advertiser Growth, Amazon Sponsored Products & Brands This role leads the development of generative AI applications for advertisers, focusing on agentic experiences for recommendations and guidance. It involves fine-tuning, reinforcement learning, and preference optimization, with a strong emphasis on creating customer-facing products and mentoring AI talent. | AgentPost-train | 8 |
| Software Engineer- AI/ML, AWS Neuron Software Engineer role focused on building and tuning distributed training solutions for AWS Inferentia and Trainium accelerators, specifically for large language models and other ML model families. The role involves working with PyTorch, Jax, XLA, and the Neuron compiler/runtime to maximize performance and efficiency on AWS Trainium. | PretrainServe | 8 |
| Sr. SDE- ML Data Infrastructure, Frontier AI Robotics Senior Software Development Engineer focused on ML Data Infrastructure for Frontier AI Robotics at Amazon. The role involves building and maintaining scalable data infrastructure, designing dataset management systems, developing visualization tools, and implementing advanced data filtering techniques to support cutting-edge AI robotics research. Collaboration with science teams is key, requiring both infrastructure development and hands-on technical contribution to data preparation. | Data | 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 |
| Senior Applied Scientist, Last Mile Delivery Automation Senior Applied Scientist role focused on developing and deploying machine learning models for Amazon's autonomous delivery systems, specifically in perception, prediction, and decision-making for autonomous vehicles. The role involves designing algorithms, leading research, transforming concepts into production, creating evaluation frameworks, and collaborating with engineering teams. | AgentServe | 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 |
| Sr. SDE, Simulation, Frontier AI Robotics Seeking a Simulation Engineer to join an AI robotics research team, focusing on developing 3D physics-based simulation frameworks and tools to enable large-scale machine learning model training for robotics. The role involves developing simulations for reinforcement learning, closed-loop simulations, synthetic data generation, implementing robotics features, building real-to-sim workflows, and collaborating with ML researchers. | DataPost-train | 8 |
| Sr. Software Development Engineer, Data Center Design Engineering - BIM & AI Technologies Senior Software Development Engineer to lead the design and implementation of generative AI applications for data center design automation, integrating BIM platforms and AWS services. The role involves productionizing ML models, mentoring junior engineers, and building scalable systems in a greenfield opportunity. | Agent | 7 |
| 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 |
| Applied Scientist, TSI Science The role focuses on building and deploying end-to-end machine learning solutions to prevent eCommerce fraud, leveraging GenAI/LLM/VLM technology for risk evaluation and automated operations. It involves analyzing large datasets, developing and validating models, and impacting business profitability. | Agent | 7 |
| Sr. Delivery Consultant - AI/ML, WWPS ProServe Senior Delivery Consultant for AI/ML within AWS Professional Services, focusing on designing, implementing, and scaling Generative AI solutions for enterprise customers. The role involves working directly with clients to understand their needs, select and fine-tune models, develop proof-of-concepts, and provide technical guidance throughout the project lifecycle. Requires a Top Secret security clearance. | ShipPost-train | 7 |
| Delivery Consultant- AI/ML, WWPS ProServe Delivery Team This role focuses on designing, implementing, and scaling AI/ML solutions for enterprise customers on AWS, with a strong emphasis on generative AI. The consultant will work with customers to identify use cases, select, fine-tune, and deploy models, and provide technical guidance throughout the project lifecycle. | Post-trainAgent | 7 |
