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.

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.

This role focuses on developing and scaling a machine learning compiler for AWS Neuron, which optimizes the performance of neural network models on custom AWS hardware accelerators (Inferentia and Trainium). The engineer will architect and implement features for the compiler stack, which integrates with popular ML frameworks, aiming to improve inference and training performance for large ML workloads.

Sr. Applied Scientist at Amazon Prime Video focused on developing and launching AI solutions for personalization and discovery systems, impacting millions of customers.

This role focuses on managing AI-focused customer engagements end-to-end, partnering with engineers and designers to deliver AI solutions using technologies like LLMs, RAG, and autonomous agents. The role involves orchestrating customer engagements, facilitating solution design, identifying opportunities, building relationships, and ensuring responsible AI practices.

Sr. Applied AI Solutions Architect focused on accelerating customer adoption of Amazon Connect's AI capabilities. The role involves guiding customers in model selection (via Amazon Bedrock), prompt configuration for AI agents, and architecting tool integrations (APIs, Lambda, etc.) for agentic AI systems. A key aspect is ensuring customer data readiness for AI agents and RAG. The role is hands-on, requiring coding, building integrations, and configuring agents, working at the intersection of contact center operations and applied AI.

Director of Science, Geospatial at Amazon, leading a team of ~50 scientists focused on AI/ML solutions for last-mile delivery operations. The role involves developing and deploying solutions for geospatial problems, including address validation, place datasets, road networks, and leveraging edge data. Key focus areas include GenAI (LLMs, VLMs, agents), computer vision, and traditional ML to optimize delivery routes, improve data fidelity, and drive business impact. The role requires interfacing with senior stakeholders, strategic planning, and building a high-performing team.

Senior Software Development Engineer to lead AI/ML/LLM/VLM/VLA development for automating manufacturing engineering workflows, including agentic AI, robotic control, and computer vision for quality assurance.

This role manages a team of applied scientists and engineers focused on developing ML and RL algorithms for robotic systems to optimize stow strategy and warehouse capacity. It involves leading research, design, deployment, and evaluation of these systems, with a focus on transformer architectures, affordance learning, and geometric reasoning in high-density environments.

Senior Applied Scientist role focused on developing and deploying AI/ML models and analytics for customer-facing AI solutions within Amazon Connect. The role involves working directly with customers to accelerate production deployments, designing and building AI solutions, conducting experiments, quantifying business value, and applying NLP/generative AI techniques. It spans conversational analytics and agentic AI capabilities, with a strong emphasis on driving measurable business impact and operational excellence in customer environments.

This role focuses on applying Generative AI and LLMs to transform ad experiences on Amazon's homepage and other surfaces, impacting product discovery and customer engagement. It involves building and deploying models for ad retrieval, auctions, and personalized shopping experiences, operating across the full stack from backend systems to the user-facing layer.

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.

Senior Applied AI Solutions Architect for Federal Financial Regulatory customers, focusing on designing and enabling AI/ML solutions for fraud detection, market surveillance, regulatory reporting, and consumer protection. The role involves technical guidance, developing reference architectures, and enabling customer adoption of AI/ML on AWS, with a strong emphasis on agentic systems and RAG.

Manager for the Amazon Sponsored Agent (ASA) team, focusing on building and scaling a new agentic service for conversational and agentic ads. The role involves leading a team to develop a multi-agent system architecture for contextual ad serving, conversation understanding, and commercial insights generation, with a focus on AI-native ad formats.

Senior Applied Scientist role at Amazon's FinTech organization, focusing on building and scaling generative AI applications and autonomous agents for financial operations. The role involves developing systems that process financial transactions, extract intelligence from documents, and power agents that learn from customer interactions. Key responsibilities include ensuring AI systems are trusted for compliance, designing agents that improve with user feedback, optimizing inference at scale using tiered models and LLMs, and developing robust evaluation frameworks. The position emphasizes shipping production-ready models, working across the full stack, and solving complex real-world financial problems.

This role focuses on developing advanced robotics systems that integrate AI, control systems, and mechanical design for automation. The scientist will define the scientific roadmap for whole body control and dexterous manipulation, applying deep learning and LLMs to solve complex operational challenges in dynamic environments. The role involves research and practical implementation of AI in physical robotic hardware, with a focus on shipping these systems.

Senior Manager to lead an AI-first security technology organization, owning the enterprise AI/ML roadmap, leading a team of scientists and BIEs, and delivering production AI/ML models for efficiency gains and loss reduction. The role involves establishing AI/ML delivery standards, building MLOps infrastructure, and partnering with business and technical leaders, while ensuring responsible AI and compliance with regulations.

This role focuses on designing and implementing innovative AI solutions, developing ML models and frameworks, enabling self-service automation, and building evaluation frameworks to enhance productivity and unlock new value within Audible. The role involves applying ML/AI approaches to solve complex real-world problems and building the blueprint for how Audible works with AI.

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.

Software Development Engineer role focused on building the platform for validating safety-critical autonomous systems. This involves designing scenario generation pipelines, integrating generative AI models for realistic behaviors, creating synthetic sensor data, and developing export connectors for simulation platforms. The role spans the full lifecycle from data curation to deployment monitoring, with a focus on automating testing and exploring edge cases.

Senior Applied Scientist role focused on improving advertising performance and delivering innovative advertising experiences for Amazon devices and grocery. The role involves building and deploying machine learning models, with a specific emphasis on agentic AI for ads targeting, including autonomous agents, multi-agent orchestration, large multimodal models, reinforcement learning, and sequential decision making. The position requires experience in developing scalable data pipelines, optimizing conversion KPIs, and staying updated with the latest advancements in ML, NLP, and multimodal learning.

Machine Learning Engineer role focused on designing, implementing, and scaling AI/ML solutions for AWS customers. This involves selecting, fine-tuning, and deploying models, identifying use cases, and providing technical guidance on responsible AI adoption. The role requires experience with ML/statistical modeling, software engineering best practices, and a Top Secret security clearance.

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.

Machine Learning Engineer on AWS Professional Services team, focusing on designing, implementing, and scaling Generative AI solutions for customers. Requires TS/SCI clearance.

Applied Scientist II role focused on building AI-based automated customer service solutions using RAG, agentic AI, and post-training of LLMs. Responsibilities include designing and deploying RAG pipelines, conducting LLM post-training, curating datasets, implementing evaluation frameworks, developing AI agents, and collaborating with cross-functional teams. The role involves research and development with minimal guidance, aiming to translate research into production systems and contribute to the scientific community.

The Data Scientist II will focus on developing evaluation and benchmarking datasets for enterprise AI features, specifically for Amazon Quick Suite. This involves leveraging Generative AI techniques, LLMs for synthetic data generation, and LLM-as-a-judge settings to assess model performance, ensure data quality, and contribute to Responsible AI initiatives. The role also includes building scalable data pipelines and tools for continuous evaluation.

Applied Scientist role focused on building and deploying AI/ML models for customer behavior prediction and personalization within Amazon Prime. The role involves working with large-scale data, leveraging GenAI, LLMs, deep learning, and reinforcement learning, and contributing to production AI/ML systems. Emphasis on scientific research, publication, and utilizing AWS technologies.

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.

Applied Scientist focused on developing learning-based approaches for mobile manipulation in robotics, aiming to advance capabilities for robots navigating and manipulating objects in dynamic fulfillment environments. The role involves model development, training, data management, experimentation, validation, and code development for production systems at Amazon's scale.

This role focuses on accelerating customer adoption of Amazon Connect's AI capabilities by acting as an Applied AI Solutions Architect. The architect will guide customers in selecting foundation models, designing and optimizing AI prompts, and architecting tool integrations for agentic AI systems. A key aspect is ensuring customer data readiness for AI agents and helping customers move from proof-of-concept to pre-production for Amazon Connect + Unlimited AI deployments. The role involves hands-on coding, building integrations, configuring agents, and collaborating with customer engineering teams.

This internship focuses on developing and implementing cutting-edge computer vision algorithms and models for Amazon's consumer-facing products and services, such as Rekognition, Go, and Visual Search. The role involves working with large-scale systems, including mobile robots and advanced tooling, to solve real-world problems. Interns will contribute to production-level projects, technical white papers, and roadmaps, with a strong emphasis on applied science and deep learning in computer vision, potentially involving Vision-Language Models and LLMs.

This internship focuses on developing and evaluating new recommendation and search algorithms, building data processing pipelines, and conducting research in recommender systems and information retrieval. The role involves applying machine learning, deep learning, and NLP techniques to large-scale datasets to improve personalized experiences for Amazon customers.

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.

Senior Machine Learning Engineer at AWS Identity Analytics Platform, focusing on building an AI-driven analytics platform that processes petabyte-scale data to generate insights for security and operational problems. The role involves designing, developing, and deploying ML solutions, including anomaly detection, time-series forecasting, classification, optimization models, and LLM-powered agents for conversational data querying. It also includes feature engineering, production deployment, and collaboration with leadership and service teams.

Senior Applied AI Solutions Architect for Amazon Connect, focused on accelerating customer adoption of AI capabilities. The role involves guiding customers in model selection, prompt configuration, and tool integration for AI agents, with a strong emphasis on customer data readiness and enabling multi-agent orchestration. This is a hands-on role requiring coding, integration building, and pair-programming with customer teams to move from proof-of-concept to production.

Senior ML Engineer to build and scale ML systems for intelligent robots, focusing on designing and maintaining infrastructure for training, evaluating, and deploying ML models. The role involves working at the intersection of ML and systems engineering to ensure robust, efficient, and scalable systems, with a focus on optimizing model inference for edge devices.

Senior AI Architect role focused on designing and building AI agents for AWS Professional Services to automate and accelerate consulting services delivery. The role involves working with customers, leading technical solutions, and architecting scalable AI/ML and GenAI solutions on AWS.

Manager for a team of applied scientists and ML engineers focused on building generative AI solutions for advertisers within Amazon Advertising. The role involves setting scientific strategy, mentoring scientists, managing talent, and delivering AI products at scale, with a focus on multi-modal generative AI for creative assets.

Senior Applied Scientist role focused on developing AI and ML systems for industrial robotics and manufacturing, involving real-time decision making, optimization, and inventing new algorithms. The role requires delivering complex solutions into production and has a strong emphasis on scientific code development and evaluation.

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.

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.

Applied Scientist role focused on designing, developing, and deploying machine learning and computer vision models for Amazon's in-store grocery technologies. The role involves end-to-end model development, from ideation to production, with a focus on solving complex grocery-domain problems at scale and improving the customer shopping experience.

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.

Software Development Manager to lead a team building agentic AI systems for AWS Glue, EMR, and Athena, focusing on automated Spark upgrade and migration agents, and a managed analytics service providing AI assistants and agents access to tools. The role involves owning design, implementation, testing, and deployment, driving technical decisions at the intersection of GenAI, distributed systems, big data, and ML, and partnering with senior leadership and customers.

Manager for an Applied Science team focused on Brand Protection ML at Amazon. The role involves leading scientists to build and launch scalable AI/ML/LLM/GenAI solutions to identify and prevent infringement and counterfeit on Amazon's platform globally. The team works on complex business problems with significant customer impact, leveraging SOTA ML techniques and deep learning, computer vision, and NLP.

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.

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.

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.