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, AGI Customization Senior Applied Scientist role focused on developing state-of-the-art services and tools for model customization (fine-tuning, RL, knowledge distillation) for Amazon Nova, enabling enterprises to build application-specific models. |
| Post-trainPretrain |
| 9 |
| ML Engineer, Fauna Machine Learning Engineer to train, evaluate, and deploy models for robots, focusing on reinforcement learning, computer vision, and supervised learning for embodied systems. Responsibilities include training policies, debugging convergence, running experiments, optimizing models for edge hardware, and building MLOps infrastructure. | Post-trainServe | 9 |
| Senior ML Engineer, Fauna Senior ML Engineer focused on training, evaluating, and deploying models for robots, with expertise in reinforcement learning, computer vision, and supervised learning for embodied systems. Responsibilities include training policies, debugging convergence, running experiments, optimizing models for edge deployment, and building MLOps infrastructure. | Post-trainServe | 9 |
| Applied Scientist II - AMZ9674020 Applied Scientist II role focused on designing, developing, and deploying data-driven models for ML and NL applications, with a strong emphasis on generative AI, NLP, and large-scale model training and deployment. The role involves researching and implementing novel ML approaches, fine-tuning foundation models, developing custom algorithms for model optimization, and conducting applied research on generative AI architectures and training strategies. Mentoring junior scientists is also a key responsibility. | Post-trainAgent | 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 on large datasets, optimizing for inference at scale, and collaborating with science and engineering teams for production deployments. It requires guiding technical direction, mentoring, and maintaining individual contributions. | Post-trainServe | 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 Science Manager, Alexa International Manager for a team of Applied Scientists focused on building and enhancing multilingual speech models (understanding and generation) for Alexa. The role involves leading the team, setting technical direction, driving scientific strategy, and ensuring end-to-end delivery of speech quality improvements from research to production. Key areas include speech-to-speech models, text-to-speech synthesis, multilingual systems, and leveraging large-scale data and computing resources. | Post-trainServe | 8 |
| Senior Machine Learning Engineer, AWS Generative AI Innovation Center Senior Machine Learning Engineer at AWS Generative AI Innovation Center focused on designing, implementing, and optimizing generative AI solutions for AWS customers. The role involves working with customers to develop bespoke solutions, including fine-tuning and optimizing SLM/LLM models, and addressing complexities in distributed training and low-latency model hosting. | Post-trainServe | 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 |
| 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 |
| 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 |
| 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 Applied Scientist, HST Health Evaluation Senior Applied Scientist role focused on developing and deploying AI/ML solutions for healthcare, specifically involving LLMs and VLMs, with an emphasis on model optimization and fine-tuning for production. | Post-trainServe | 8 |
| Data Scientist - II, Alexa Sensitive Content Intelligence The Data Scientist-II role on the Alexa Sensitive Content Intelligence (ASCI) team focuses on building AI safety systems for Alexa's next-generation AI-powered virtual assistant. This involves developing responsible AI (RAI) solutions to ensure LLMs provide safe and trustworthy responses, understanding nuanced human values, and maintaining customer trust. The role requires applying state-of-the-art Generative AI techniques to analyze data, run experiments, and optimize data for sensitive content detection and mitigation, working with LLMs and multimodal systems. | Post-trainData | 8 |
| Data Scientist - II, Alexa Sensitive Content Intelligence The Data Scientist-II role on the Alexa Sensitive Content Intelligence (ASCI) team focuses on building AI safety systems for Alexa's next-generation AI-powered virtual assistant. This involves developing responsible AI (RAI) solutions to ensure LLMs provide safe and trustworthy responses, understanding nuanced human values, and maintaining customer trust. The role requires applying state-of-the-art Generative AI techniques to analyze data, run experiments, and optimize data for sensitive content detection and mitigation, working with LLMs and multimodal systems. | Post-trainData | 8 |
| Applied Scientist II, HST Health Evaluation Applied Scientist II role focused on developing and optimizing state-of-the-art AI/ML solutions for healthcare, specifically LLMs and VLMs, with a focus on production deployment and model distillation. | Post-trainServe | 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. Machine Learning Engineer, WWPS ProServe Data and Machine Learning Senior Machine Learning Engineer role focused on designing, implementing, and scaling AI/ML solutions for AWS customers. The role involves working with customers to understand their needs, select and fine-tune models, develop proof-of-concepts, and implement AI/ML solutions at scale. It also includes designing and running experiments, researching new algorithms, and optimizing for business impact. The role requires expertise in machine learning, generative AI, and best practices, with a focus on customer success and AI transformation. | Post-trainAgent | 8 |
| Sr. Data Scientist- Computer Vision, Data & Machine Learning (DML) Develop computer vision models on overhead imagery for a government customer, owning the entire ML development lifecycle from data exploration and feature engineering to model training, evaluation, and delivery. This role operates on classified networks and requires a Top Secret security clearance. | Post-trainData | 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 |
| Senior Applied Scientist, Translation Services Senior Applied Scientist role focused on applying advanced NLP and LLM techniques to improve machine translation quality and pipeline efficiency for Amazon's e-commerce platform. The role involves architecting and implementing scalable ML solutions, driving data analysis, and pioneering modeling techniques for translation quality assessment and optimization. The scientist will also serve as an expert in LLM applications for translation and mentor team members. | Post-train | 8 |
| Sr. Applied Scientist, SSG Science This role focuses on optimizing and fine-tuning Generative AI models for edge platforms, working closely with custom ML hardware. The scientist will train custom models, analyze deep learning workloads, and collaborate with cross-functional teams to build ML-centric solutions for consumer devices. The role also involves publishing research and presenting at ML conferences. | Post-trainServe | 8 |
| Sr. Software Engineer- AI/ML, AWS Neuron Distributed Training - Performance Optimization Senior Software Engineer focused on performance optimization for distributed AI model training on AWS Trainium accelerators. The role involves working with frameworks like PyTorch and JAX, optimizing the Neuron software stack, and improving training throughput and efficiency for large-scale models. | 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 |
| Applied Scientist II, Central Machine Learning The Applied Scientist II role focuses on building and deploying machine learning models for Amazon's consumer businesses. Responsibilities include analyzing large datasets, designing, developing, evaluating, and deploying scalable predictive models, and implementing novel ML approaches. The role involves collaborating with engineering teams for real-time implementation and establishing automated processes for model development and validation. The position requires a PhD or Master's degree with significant experience in ML and programming, and a track record of patents or publications. | Post-trainServe | 7 |
| Business Research Analyst, ARTS This role involves developing and implementing ML/LLM solutions for business needs within Amazon's Global Stores division. The analyst will collaborate with experts, drive product pilots, build scalable solutions, write code, develop ML/LLM models, and optimize solutions by coordinating between science and software teams. The role requires working independently in ambiguous, fast-paced environments with ML/LLM models. | Post-train | 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 |
| Business Research Analyst - I, RBS Tech This role involves implementing classical ML models and LLM-based inferences for business problems. The analyst will develop prompts, conduct evaluations, collaborate on deployment, and monitor performance. The role requires hands-on Python and ML/LLM toolkit skills, understanding of AI/ML trade-offs, and the ability to deliver scoped ML components. | Post-trainServe | 7 |
| Business Research Analyst - I, RBS Tech This role involves implementing classical ML models and LLM-based inferences for business problems. The analyst will develop prompts, conduct evaluations, collaborate on deployment, and monitor performance. The role requires hands-on Python and ML/LLM toolkit skills, understanding of AI/ML trade-offs, and the ability to deliver scoped ML components. | Post-trainServe | 7 |
| Applied Scientist, Amazon Music Applied Scientist role at Amazon Music focusing on building, training, and deploying ML models for customer experiences and business decisions. The role involves collaborating with scientists and engineers, experimenting with modern ML techniques, and implementing scalable data pipelines and model-serving systems. It's suitable for early-career individuals with a PhD or Master's degree and 3+ years of experience in building models for business applications. | Post-trainServe | 7 |
| Applied Scientist II, Advertising Trust Build and develop ML models for content understanding and labeling in Ads, utilizing visual and textual features, scaling to multiple languages and countries. Collaborate with engineers and scientists to build, train, and deploy these models, writing production-level code for ad labeling and moderation. | Post-train | 7 |
| Applied Scientist II, Translation Services Applied Scientist II role focused on building and implementing LLM-based machine learning solutions for language translation at Amazon. The role involves data analysis, applying state-of-the-art modeling techniques, and collaborating with cross-functional teams to improve translation accuracy and efficiency for millions of customers worldwide. | 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 |
| Applied Scientist, Full-funnel Agentic Intelligence and Models Applied Scientist role focused on developing core models for Full-funnel Agentic Intelligence and Models within Amazon Advertising. The role involves understanding shopping journeys across ad products and publishers, and partnering with engineers and product managers to productize the work. Requires experience in building models for business applications and a strong publication or patent record. | Post-train | 7 |