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 |
|---|---|---|
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and scaling generative AI training infrastructure, specifically for LLMs. Responsibilities include designing and implementing stable and efficient training systems, scalable data infrastructure, and end-to-end RL post-training pipelines. The role involves collaborating with scientists and engineers to improve training efficiency, reliability, and optimize RL training stability and efficiency. It also includes building observability systems and contributing to system design and technical roadmaps for a unified LLM training platform. | Post-trainData | 9 |
| Senior Applied Scientist, Delivery Foundation Model Senior Applied Scientist role focused on developing and implementing novel deep learning foundation models, combining multiple modalities (image, video, geospatial) for logistics use cases. The role involves training models at scale, optimizing for inference, collaborating with other teams, guiding technical direction, and mentoring junior scientists. It spans the full spectrum from data preparation to model training, evaluation, and inference. |
| Post-trainServe |
| 9 |
| Sr. Applied Science Manager, Perfect Order Experience (POE) AI Senior Applied Science Manager leading a team to develop a domain-specific LLM, including pre-training, fine-tuning, and reinforcement learning. The role also involves architecting risk detection systems using multi-modal signals and influencing ranker models for product visibility. The focus is on building and scaling AI solutions for Amazon's Perfect Order Experience. | Post-trainPretrain | 9 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II focused on building and optimizing generative AI training systems, specifically for LLMs and RL post-training pipelines, at Amazon's Stores Foundational AI team. The role involves designing scalable data infrastructure, improving training efficiency and reliability, and translating research algorithms into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II at Amazon on the Stores Foundational AI team, focusing on building and optimizing large-scale LLM training infrastructure, including pretraining and RL post-training pipelines, data infrastructure, and observability systems for generative AI in shopping. | Post-trainData | 8 |
| Sr Software Dev Engineer, Stores Foundational AI -SFAI Senior Software Development Engineer focused on building and scaling ML infrastructure for foundational LLMs in Amazon Stores, specifically involving RL post-training pipelines, stability, efficiency, and translating research into production systems. | Post-trainServe | 8 |
| Software Development Manager, AWS Neuron SDK - Distributed Training Software Development Manager for AWS Neuron SDK, focusing on distributed training for ML accelerators. The role involves leading a team to design and deploy new products, optimize performance of ML models at scale, and ensure support for key ML functionality. Responsibilities include customer onboarding, maximizing model FLOPS utilization, building tooling, partnering with other teams, and driving technical strategy for frontier model architectures. | Post-trainServe | 8 |
| Applied Scientist II - GenAI/LLM, Translation Services Applied Scientist II role at Amazon focusing on designing and developing scalable machine learning solutions for language translation services using GenAI/LLMs. The role involves applying expertise in LLM models, collaborating with cross-functional teams, conducting data analysis, and evaluating state-of-the-art modeling techniques to improve translation accuracy and efficiency. The team has a startup mindset and aims to build scalable solutions from scratch. | Post-train | 8 |
| Applied Science Manager , C360 Manager for a team working on LLM and VLM post-training and alignment for personalized shopping experiences, leveraging customer behavioral data. | Post-trainAgent | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 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 |
| Machine Learning Engineer , Data & Machine Learning (DML) Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage. | Post-trainAgent | 8 |
| Applied Scientist II - GenAI/LLM, Translation Services Applied Scientist II role at Amazon focusing on designing and developing scalable machine learning solutions for language translation services using GenAI/LLMs. The role involves applying expertise in LLM models, conducting data analysis, and collaborating with cross-functional teams to improve translation accuracy and efficiency for millions of customers worldwide. | Post-train | 8 |
| Machine Learning Engineer , Data & Machine Learning (DML) Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage. | Post-trainAgent | 8 |
| Applied Scientist, AGI Customization Services Applied Scientist role focused on developing and customizing large language models for enterprise use cases, involving techniques like supervised fine-tuning, reinforcement learning, and knowledge distillation. The role requires building enterprise-ready tooling, optimizing models, and contributing to responsible AI toolkits. | Post-trainData | 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 |
| 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 |
| 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 |
| Data Scientist II, Amazon Currency Convertor Data Scientist II at Amazon Payments focused on building analytical solutions for the Amazon Currency Convertor using Gen AI, LLM, and other machine learning techniques for text analytics, segmentation, and prediction. Responsibilities include applying causal inference, developing descriptive and predictive solutions, collaborating with stakeholders, innovating with modeling techniques, performing exploratory data analysis, and building models using standard techniques. Specific tasks involve fine-tuning Amazon LLMs for text summarization, preventing catastrophic forgetting, feature engineering, and implementing data flow solutions. | Post-train | 7 |
| AI Editor, Alexa for Shopping Content and Marketing Experiences This role focuses on improving AI model fluency through human-in-the-loop evaluations and LLM judge audits, developing prompting strategies, creating alignment data for LLMs in shopping use cases, and identifying/mitigating biases through fine-tuning. It involves cross-functional collaboration with Product, Science, and Design teams to enhance customer experience metrics and ensure model improvements for Alexa for Shopping. | Post-trainAgent | 7 |
| Data Scientist II, Long Term Planning and Forecasting This Data Scientist II role focuses on building scientific tooling for how business customers interact with Long-Term Planning and Forecasting (LTPF) forecasts and plans. The role involves developing causal inference models, automated explainability frameworks, and variance bridging methodologies. It also includes building GenAI-powered narrative generation capabilities and automated hypothesis ranking to synthesize quantitative variance outputs into human-readable performance summaries and identify drivers of forecast error. The position emphasizes leading cross-functional programs, defining multi-year strategy, and leveraging insights for strategic decision-making. | Post-trainData | 7 |
| Sr. Design Technologist, Prime Video - AI Content Generation This role bridges generative AI research and visual storytelling for Prime Video, focusing on translating ML capabilities into production workflows and understanding creative needs. The Sr. Design Technologist will assess generative models, build proof-of-concept tools, and identify gaps between model output and production requirements. | Post-train | 7 |
| Sr Applied Scientist, Sponsored Products and Brands Ads Response Prediction This role focuses on developing and deploying machine learning models for Amazon's Sponsored Products and Brands Ads, aiming to improve customer experience and advertiser effectiveness. The scientist will conduct data analysis, build and optimize ML models, run A/B experiments, and collaborate with engineers to productionize solutions. They will also research new ML modeling techniques to enhance business outcomes. | Post-train | 7 |