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 |
|---|---|---|
| Machine Learning Software Development Engineer, AI Ops Integration Machine Learning Software Development Engineer to build and deploy ML/LLM-powered features, implement AI agent components for automating workflows, develop internal front-end applications, build integrations for multi-system orchestration, and contribute to the ML lifecycle including guardrails and evaluation frameworks. The role focuses on solving operational problems at scale within Amazon Operations & Supply Chain. | AgentServe | 8 |
| Principal Solutions Architect, AWS Financial Services, Industry Specialists for Capital Markets Principal Solutions Architect for AWS Financial Services, specializing in Capital Markets. This role focuses on designing and architecting AWS solutions for clients in hedge funds, asset management, and quantitative trading firms, with a strong emphasis on data and analytics, generative AI, and high-performance computing. Responsibilities include migrating data-intensive workloads, architecting generative AI/ML solutions (LLM fine-tuning, RAG, sentiment analysis, agentic AI), and designing HPC environments. The role involves deep technical expertise and customer engagement to accelerate modernization on AWS. |
| AgentData |
| 8 |
| Senior Applied Scientist, Sponsored Products and Brands Senior Applied Scientist role focused on architecting and pioneering applied science in multi-modal Generative AI for Amazon's Sponsored Products and Brands advertising platform. The role involves end-to-end innovation from research to production deployment at Amazon scale, with a focus on non-US marketplaces and leveraging technologies like LLMs and semantic hash designs. The candidate should have experience building ML models, deep technical expertise, and a proven track record of delivering value with state-of-the-art technologies in production environments. | ShipAgent | 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 |
| Sr. Applied Scientist, Alexa Excellence AI Ops, Alexa Excellence AI Ops Senior Applied Scientist role focused on developing and deploying ML and statistical models for Alexa's reliability at scale. This involves time series analysis, anomaly detection, LLM-driven operational intelligence, and adaptive thresholds, with a focus on production-grade solutions and rigorous evaluation. | AgentData | 8 |
| Applied Science Manager, AWS Generative AI Innovation Center Manager for an AWS Generative AI Innovation Center focused on building and deploying generative AI solutions for customers, managing a team of scientists and engineers, and driving adoption of AI technologies. | ShipPost-train | 8 |
| Applied Scientist II, Sheriff Team- Payroll tech This role focuses on developing and maintaining ML and Generative AI applications for Payroll Operations at Amazon. Key responsibilities include inventing, implementing, and influencing ML/GenAI capabilities for anomaly detection, sentiment analysis, ticket classification, virtual assistance, and automated policy extraction. The role involves driving model accuracy, scientific innovation, and global scale, with a focus on integrating ML components into production systems and influencing strategic planning for AI capabilities. | AgentPost-train | 8 |
| Sr. Software Dev Engineer, Applied AI Senior Software Development Engineer on the Applied AI team, focusing on Knowledge Work Automation. The role involves designing, building, and shipping multi-agentic systems and AI agents for internal Amazon workflows, leveraging LLMs and production-grade distributed software. Key responsibilities include architecting agentic AI systems, innovating with LLM techniques, shipping reusable primitives, and driving end-to-end delivery. | Agent | 8 |
| Sr. Software Engineer, Trust CX Innovations & AI Policies Senior Software Engineer to build foundational systems and consumer-facing features for trustworthy AI experiences at scale. Focus on privacy-preserving AI, responsible AI frameworks, and accessibility. Key challenges include latency vs. privacy trade-offs, AI safety, ambient computing privacy, multimodal AI systems, and real-time evaluation. | AgentEval Gate | 8 |
| Senior Applied Scientist, Leo Satellite Build Intelligence Senior Applied Scientist to lead the development of AI models for satellite manufacturing, transforming data into an intelligence system that improves how satellites are built. Focuses on AI-native workflows like non-conformance disposition, root-cause analysis, and predictive test optimization, influencing real-world manufacturing decisions. | AgentData | 8 |
| Software Development Engineer, Sponsored Products and Brands Software Development Engineer II to design and build AI-powered advertiser controls, including bidding systems, agentic architectures, and experimentation systems for Amazon's Sponsored Products and Brands. The role involves owning platform capabilities, developing AI engineering infrastructure, interfacing agentic architectures, and designing experimentation systems. Requires strong software engineering skills, experience with Gen AI/LLMs, fine-tuning, RLHF, and RAG, with a focus on delivering customer-facing AI products. | AgentServe | 8 |
| Applied Scientist II, Trustworthy Shopping Experience (TSE) Applied Scientist II on Amazon's Trustworthy Shopping Experience (TSE) team, focusing on building and productionizing generative AI solutions for automating complex manual investigation processes at scale. The role involves designing and building agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, leveraging techniques like SFT, RFT, and few-shot approaches. The scientist will also work on prompt optimization, novel Finetuned transformer architectures, and identifying business problems to apply state-of-the-art LLM workflows. The role offers end-to-end ownership from research to production deployment, with a focus on impacting cost-of-serving customers while maintaining trust and safety. | AgentPost-train | 8 |
| Senior Manager, Applied Science, Prime Video Advertising Senior Manager, Applied Science at Amazon Prime Video Advertising, leading a team to build and scale ML/AI solutions for advertising optimization, experimentation, and generative AI-powered ad creative generation. The role involves setting scientific vision, managing managers, and driving strategic initiatives in a rapidly growing business. | Ship | 8 |
| Applied Scientist I, Alexa Ads Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. The role involves designing, developing, and evaluating deep learning models for NLP and recommendation systems, building ML pipelines, running A/B experiments, and deploying models to production. The team is greenfield, aiming for direct business impact and encouraging top-tier publications alongside production deployment. | AgentEval Gate | 8 |
| Applied Scientist II, Alexa Ads Applied Scientist II at Amazon Alexa Ads focused on building Generative AI models for conversational ads and personalization. The role involves designing, developing, and evaluating deep learning and GenAI models, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating with engineers for production deployment. The team is greenfield, aiming to rethink ad ranking, pricing, and personalization for voice and screen surfaces, with opportunities for both shipping products and publishing research. | ShipAgent | 8 |
| Senior Applied Scientist, Alexa Ads Senior Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. The role involves designing, developing, and evaluating deep learning and GenAI models, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating with engineers for production deployment. The team is greenfield, aiming for direct business impact and encouraging both production deployment and top-tier publications. | ShipAgent | 8 |
| Generative AI Solutions Architect, AWS Global Government Specialist SA This role focuses on designing and implementing generative AI solutions for government customers using AWS services. The Solutions Architect will act as a Subject Matter Expert, guiding customers and internal teams on leveraging AI for automation and cost reduction. The role requires deep technical experience in AI/ML and integrating these services into production applications. | Ship | 8 |
| Generative AI Solutions Architect, AWS Global Government Specialist SA This role focuses on designing and implementing generative AI solutions for government customers using AWS services. The Solutions Architect will act as a Subject Matter Expert, guiding customers and internal teams on leveraging AI for automation and cost reduction. The role requires deep technical experience in AI/ML and integrating these services into production applications. | Ship | 8 |
| Applied Scientist, SSG Science Applied Scientist role focused on optimizing Generative AI models for edge devices, involving quantization, pruning, distillation, and fine-tuning. The role also requires understanding and inventing optimization techniques for custom ML hardware and collaborating with hardware architects and compiler engineers. The goal is to develop production-ready edge models and publish research findings. | Post-trainServe | 8 |
| Senior Data Scientist , Alexa AI Aurora Senior Data Scientist role focused on conversational AI, LLMs, NLP, and Generative AI for Alexa. The role involves defining strategy, leading initiatives from problem formulation to production, establishing evaluation frameworks, and driving consensus on agentic systems. It requires expertise in machine learning, generative AI, and computer vision, with a focus on delivering scalable and impactful solutions for millions of customers. | AgentPost-train | 8 |
| Applied Scientist, Alexa Ads Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. Responsibilities include designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, building ML pipelines, running A/B experiments, and collaborating on production deployment. | AgentPost-train | 8 |
| Software Development Manager, Data Center - GenAI Manager for a team building an agentic GenAI platform for AWS data center operations, focusing on LLM orchestration, agent frameworks, search/knowledge systems, and full-stack serverless engineering. | Agent | 8 |
| Applied Scientist II, Amazon Travel & Events Applied Scientist II role focused on building AI-driven solutions for Amazon Travel & Events, leveraging Generative AI, LLMs, NLU, conversational AI, and Applied ML. Responsibilities include designing, developing, and evaluating ML models using GenAI, multimodal reasoning, and information retrieval for catalog understanding, applying VLMs and LLM-based approaches with fine-tuning and RAG, implementing model optimization techniques for efficiency, driving experiments, building ML pipelines, contributing to model reliability through interpretability and calibration, and collaborating with teams to translate business requirements into ML solutions. The role also involves staying current with research and co-authoring publications. | AgentServe | 8 |
| Sr Applied Scientist - Robotics Simulation, Amazon Robotics R&D Senior Applied Scientist role focused on developing 3D physics-based simulation environments and tools for robotics, specifically for training large-scale machine learning models using reinforcement learning and synthetic data generation. The role involves establishing processes, building real-to-sim workflows, and minimizing sim-to-real gaps, with a secondary focus on enabling agentic systems through simulation. | DataAgent | 8 |
| Senior Applied Scientist, AWS Agentic Automated Reasoning Group Senior Applied Scientist role focused on building scalable neuro-symbolic systems that fuse formal reasoning with GenAI and agentic AI for AWS customers, aiming to deliver reliable, verifiable outcomes and enhance features like hallucination detection and guardrails. The role involves end-to-end ownership from research to production, collaboration, and mentoring. | AgentEval Gate | 8 |
| Software Development Engineer, Sponsored Products and Brands Software Development Engineer role focused on building an Agent platform using Generative AI and ML technologies for Amazon Ads. The role involves designing and developing scalable systems for personalized ad experiences, optimizing for cost and latency, and pioneering new approaches to conversational AI. The team owns the SPB Agent, which powers reasoning behind agentic experiences in Ads Console, Sales, and Seller Central. | Agent | 8 |
| Applied Scientist II, Sponsored Products and Brands-Agent The role focuses on building a personalized and context-aware agentic advertiser guidance system using LLMs and sophisticated tooling for Amazon Ads. It involves integrating LLMs with tools, operating across various advertiser-facing platforms, and delivering solutions through advanced agent architectures and model customization. | Agent | 8 |
| Applied Scientist, EU INTech Consumer Selection Discovery, EU InTech Consumer Selection Discovery Amazon is seeking Applied Scientists to build software and machine learning models for customer discovery experiences, focusing on ranking, recommendations, and computer vision. The role involves developing and deploying state-of-the-art models, including text-to-image and image-to-text, to enhance customer engagement with the Amazon catalog. | Ship | 8 |
| Software Development Engineer, ML Systems, Annapurna Labs Software Development Engineer role focused on building AI agents and tools to simplify and accelerate customer adoption of Amazon's AWS Neuron ML software stack, which supports Trainium and Inferentia ML chips. The role involves applying Generative AI to AI itself, identifying obstacles, and developing solutions to improve the porting and optimization of ML workloads on AWS ML silicon. | Agent | 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 |
| Data Scientist II, Central Seller Fulfillment Data Scientist II role focused on building and productionizing personalized Gen AI systems for Amazon's global selling partners, with a focus on Agentic, RL, and forecasting products. Requires expertise in ML/DL frameworks, agentic frameworks, and experience with complex AI systems and data pipelines. | AgentShip | 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 |
| Principal Applied Scientist, Sponsored Products and Brands The Principal Applied Scientist will lead the development and deployment of generative AI technologies for Amazon Ads, specifically within the Sponsored Products and Brands team. This role involves reinventing advertising experiences by integrating AI into the ad lifecycle, from creation to performance analysis. The scientist will invent new product experiences, bring state-of-the-art GenAI models to production, and define the long-term science vision for the advertising business, collaborating closely with science, product, and engineering teams to deliver high-impact products. | Ship | 8 |
| Sr. Mgr, Applied Science, Personalization Senior Manager of Applied Science at Amazon, leading a multidisciplinary team to build the next generation of personalized shopping experiences. The role involves developing state-of-the-art LLM-based techniques, deep learned transformer models for customer intent, and large-scale real-time multi-task ranking systems. The goal is to create AI primitive systems that empower other teams and directly impact millions of customers through personalized features. | ShipServe | 8 |
| Principal Applied Scientist, Amazon Payments Principal Applied Scientist for Amazon Payments AI/ML Team, focusing on AI services for payments, recommendation, prediction, and GenAI for SOP automation. The role involves setting AI direction, mentoring scientists, partnering with engineering to deliver ML/GenAI features, identifying research directions, creating roadmaps, and bringing research to production. Requires a PhD or MSc with 10+ years of experience, a track record of thought leadership, and strong collaboration skills. | Ship | 8 |
| Applied Science Manager, Sponsored Products and Brands Manager for a Continuous Model Evaluation and Learning workstream within Amazon Ads' Sponsored Products and Brands team. The role involves leading a team of applied scientists and engineers to build and ship an evaluation and remediation framework for an agentic brand-intelligence system. This includes designing evaluation metrics, developing optimization engines for prompts and synthetic data, and ensuring offline-to-online consistency for quality improvements. The goal is to enable autonomous detect-diagnose-remediate loops to scale quality across brand skills. | Eval GateAgent | 8 |
| Senior Security Engineer, AI Red Team, Threat Operations Senior Security Engineer focused on offensive security operations and research for AI systems, including training pipelines, inference systems, and model architectures. The role involves discovering and exploiting vulnerabilities, developing automation for threat emulation, and collaborating with engineering teams to improve AI security posture. | Agent | 8 |
| Applied Scientist, Customer Behavior Analytics Scientist role focused on designing and developing machine learning solutions for customer behavior analytics, utilizing deep learning, LLMs, recommendation systems, and reinforcement learning. Key responsibilities include fine-tuning generative models, developing recommendation and decision models, building behavioral representations, applying post-training optimization, and creating evaluation frameworks. The role emphasizes measurable business impact and customer satisfaction. | Post-trainAgent | 8 |
| Data Scientist II, Enterprise Security Products Data Scientist II role focused on building AI-first security products, including agentic systems, anomaly detection, and threat classification. The role involves the full ML lifecycle, from problem framing to production deployment and monitoring, with an emphasis on using AI tools to accelerate development. Key responsibilities include powering agentic architectures with models, embeddings, RAG pipelines, and evaluation frameworks, rapid prototyping, and customer validation. The role also involves partnering across disciplines and communicating complex results. The team operates with startup speed at Amazon scale, emphasizing rapid iteration and shipping. | AgentData | 8 |
| Data Scientist II, Enterprise Security Products Data Scientist II role focused on building AI-first security products, including designing, training, and shipping ML models for agentic systems, anomaly detection, and threat classification. The role involves owning the full ML lifecycle, using AI tools to accelerate development, and powering multi-agent security systems with RAG pipelines and evaluation frameworks. It emphasizes rapid prototyping, customer validation, and collaboration across disciplines. | AgentPost-train | 8 |
| AI Solution Architect AI Specialist Solutions Architect for AWS, focusing on helping enterprise customers adopt and scale GenAI, ML, and Agentic technologies. The role involves designing technical architectures, advising on best practices, and acting as a trusted advisor to customers, with a strong emphasis on production deployment and operational efficiency. | AgentServe | 8 |
| Senior Software Engineer, Leo Satellite Build Intelligence Senior Software Engineer role focused on building AI systems for satellite manufacturing intelligence. The role involves architecting and implementing a platform that connects design, production, test, and quality data, utilizing AI-native workflows with retrieval systems, foundation models, agentic tool use, and human review. Key responsibilities include designing AI-native workflows, creating evaluation mechanisms for AI quality, and building production software. The role emphasizes building agentic systems (L4) with a strong focus on evaluation and quality gates (L5). | AgentEval Gate | 8 |
| Data Scientist, AWS Quick Data The Data Scientist will focus on developing evaluation and benchmarking datasets for generative AI capabilities within the Amazon Quick Suite enterprise AI platform. This includes leveraging LLMs for synthetic data generation, creating ground truth datasets, leading human annotation initiatives, and contributing to Responsible AI efforts to ensure enterprise-readiness, safety, and effectiveness of AI at scale. | Eval GateData | 8 |
| Applied Scientist II, Payment Risk Machine Learning Applied Scientist II role focused on building and deploying machine learning models and agentic AI systems for payment risk management and fraud detection at Amazon. The role involves end-to-end development, from data analysis and model design to production deployment and monitoring, utilizing techniques like deep learning, LLMs, graph neural networks, and multi-agent systems. | AgentServe | 8 |
| Senior Applied Scientist, AWS Security Senior Applied Scientist role focused on building AI-powered tooling for AWS Security operations, including generative AI incident response assistants, natural language-driven response, detection enrichment pipelines, and security data analytics platforms. The role involves defining and executing the ML/AI roadmap, extending and inventing techniques at the product level, and bringing models from research into production systems. Responsibilities include LLM-powered incident triage, anomaly detection, RAG, prompt engineering, fine-tuning, developing evaluation frameworks, and mentoring engineers. | AgentServe | 8 |
| SDE II, Same Day Delivery Software Development Engineer II role on the Same Day Delivery Experience team, focusing on building and scaling AI-powered tools using LLMs, RAG, NLP, prompt engineering, and agentic AI workflows to enhance customer experience and protection. Responsibilities include designing and building AI tools, developing retrieval pipelines, prototyping agentic AI capabilities, and working on scalable AI/ML systems. | Agent | 8 |
| Applied Scientist, Customer Behavior Analytics This role focuses on designing and developing machine learning solutions for customer behavior analytics at Amazon. Key responsibilities include fine-tuning language and generative models, developing recommendation and decision models, building temporal representations of customer behavior, and applying post-training optimization techniques. The role also involves developing evaluation frameworks and working with business and engineering teams to drive personalized customer experiences and business impact. | Post-trainAgent | 8 |
| Applied Scientist, GenAI Evaluation Media Applied Scientist role focused on Generative AI for visual media, specifically in 3D Generative AI and Inverse Rendering. The role involves building scalable CVML models, automating their application, and designing/building pipelines to train and deploy ML models. Expertise in areas like Neural Fields, NeRFs, GANs, Diffusion Models, and differentiable rendering is required. The role bridges computer graphics, computer vision, and deep learning to improve customer experience with product imagery and videos. | Post-trainServe | 8 |
| Applied Scientist II, Brand Registry The Applied Scientist II role on the Brand Registry team at Amazon focuses on designing, developing, and deploying AI solutions, specifically leveraging LLMs and agentic AI frameworks to create intelligent automation and autonomous outcomes for brand protection and seller experience. The role involves owning the end-to-end ML lifecycle, from problem formulation to production deployment, and collaborating with product managers and engineering teams. | Agent | 8 |