Senior Principal Scientist role focused on pioneering AI technologies for secure enterprise collaboration, including novel AI architectures, human-AI interaction, AI agent orchestration, and privacy-preserving ML. The role involves translating business requirements into AI deliverables, inventing new product experiences, and bringing state-of-the-art LLM/GenAI models to production, while defining long-term science vision and collaborating with academic partners.

Internship role focused on developing novel algorithms and modeling techniques at the intersection of LLMs and generative AI for robotics, tackling research problems in robotic perception, manipulation, and control. Involves collaboration with cross-functional teams and leveraging expertise in deep learning, reinforcement learning, computer vision, and motion planning.

Lead a research team focused on advancing foundational capabilities for useful AI agents by combining LLMs with RL. The role involves managing research, aligning roadmaps, mentoring, and hiring, with a focus on evolving agents for reasoning, planning, and world modeling. Experience with training large models, scaling foundational models, and applying post-training techniques is required.

Research scientist role focused on state-of-the-art LLM technologies, integrating structured and unstructured information (e.g., RAG) for applications across Amazon businesses, with a focus on delivering innovations from research to production.

Lead the development of breakthrough foundation models for robotics, focusing on perception, manipulation, and interaction with the world. This role involves hands-on research, algorithm design, and scaling models for real-world deployment at Amazon scale, with a focus on multi-modal and efficient architectures.

Applied Scientist II role focused on developing and evaluating LLMs and multimodal systems for Alexa's international products. Responsibilities include analyzing customer behavior, building evaluation metrics, fine-tuning/post-training LLMs (SFT, DPO, RLHF, RLAIF), setting up experimentation, and contributing to research and production delivery. Requires strong ML, NLU, LLM architecture, and evaluation knowledge, with a focus on international customer nuances and diverse data sources.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology.

The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology.

Research scientist role focused on developing neuro-symbolic AI systems that integrate proof assistants for enhanced learning and reasoning, applied across various Amazon domains. The role involves defining and implementing new applications, delivering scientific artifacts, and working in an agile environment.

Internship role focused on applying state-of-the-art Deep Learning, Recommender Systems, Information Retrieval, and NLP algorithms to large datasets for customer-facing products at Amazon. Involves developing novel solutions, building prototypes, contributing to research, and potentially delivering solutions to production. Collaboration with experienced scientists and opportunities for publication in top conferences.

Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety within Amazon. The role involves evaluating existing algorithms, designing new ones, generating synthetic data, and potentially using active learning and grounding LLMs for business use cases. Collaboration with engineers and product managers is key, with an emphasis on publishing research.

This role focuses on developing and applying ML models, including foundation models, for recommendation and search systems within Prime Video's personalization and discovery science team. The goal is to enhance customer experience by recommending titles effectively and enabling discovery of niche interests. The role involves end-to-end ownership, experimentation, and collaboration with scientists, engineers, and product managers, with an emphasis on publishing research findings.

Senior Applied Scientist role focused on developing and launching foundation models for content understanding and customer behavior prediction within Prime Video. The role involves hands-on machine learning, research leadership, and end-to-end ownership of solutions, with an emphasis on publishing research findings.

Applied Scientist II at Amazon Prime Video focusing on personalization and discovery. The role involves developing foundation models for content understanding (video, text) and customer behavior prediction using deep learning and multimodal techniques. Responsibilities include building time sequence models, end-to-end solution implementation with engineers and product managers, designing and conducting A/B experiments, and publishing research findings. The team works on recommendation science for Prime Video surfaces and devices, aiming to solve cold-start problems and discover niche customer interests.

This role focuses on developing and applying ML models, including foundation models, for recommendation and search systems within Prime Video's personalization and discovery science team. The goal is to enhance customer experience by recommending titles effectively and enabling discovery of niche interests. The role involves end-to-end ownership, experimentation, and collaboration with scientists, engineers, and product managers, with an emphasis on publishing research findings.

The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research.

The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research.

Research and develop generative AI technology for Amazon Connect, focusing on LLM Agents and their evaluation/optimization to disrupt customer service experiences. The role involves building ML models from conception to deployment, prototyping, and iterating on state-of-the-art Agentic AI systems.

The role focuses on developing and applying cutting-edge simulation methodologies for advanced robotics systems, including physics-based simulation, sim-to-real transfer, and machine learning. The goal is to enable rapid development, testing, and validation of robotic systems in complex environments. The role involves fundamental research and real-world development, translating research into scalable simulation capabilities that impact robot design and building.

Seeking an Applied Scientist with expertise in AI, Agentic LLMs, Generative AI, Machine Learning, and NLP to build LLM-powered solutions for Amazon's BuyForMe product. The role involves developing agentic frameworks, LLM fine-tuning, reinforcement learning, prompt engineering, RAG, MCP, and automated benchmarking to improve shopping workflows.

This role leads the science strategy and technical vision for an intelligence layer using GenAI and predictive modeling, focusing on heterogeneous signals to power talent applications at Amazon scale. The Principal Applied Scientist will guide a team, conduct hands-on research in areas like foundation models and multi-modal LLMs, design novel ML architectures, and mentor scientists while contributing technically to complex problems.

Applied Scientist II role focused on developing advanced robotics systems using AI, deep learning, and reinforcement learning for automation at Amazon's scale. The role involves designing and implementing control methods for balance, locomotion, and manipulation, with a focus on bridging theoretical advancements and practical implementation in robotics.

This role leads the science function in WW Stores Finance, driving AI/ML innovations in financial analytics. The leader builds and directs a multidisciplinary team to deliver scalable solutions, translating AI capabilities into production systems. The role requires strategic vision and execution excellence to transform finance operations, automate workflows, and improve forecasting and controllership through agentic AI, ML, and generative AI.

This role is for a Sr. Applied Scientist on an Amazon Special Projects team focused on creating new products and services. The role involves leading research projects from ideation to production, driving ML/AI strategy, collaborating cross-functionally, publishing findings, and establishing best practices for ML experimentation and deployment. Requires a PhD or Master's with significant applied research experience, strong programming skills, and experience with ML/LLM fundamentals and deploying ML systems at scale. Experience with autonomous AI frameworks and translating research into production systems is preferred.

Principal Applied Scientist role focused on AI-powered design automation for AWS data centers. The role involves defining research roadmaps, developing and deploying ML models (including fine-tuning foundation models, GNNs, NLP, RL, CV) for BIM and AECO applications, and publishing research findings. It requires a blend of theoretical ML knowledge and practical application in a domain with high trust requirements.

This role focuses on advancing knowledge graphs for the LLM era, specifically for LLM grounding and construction pipelines. It involves web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory over graphs. The primary responsibility is entity resolution for conflating facts from multiple sources into a single graph entity, requiring scalable, generic, and streaming data solutions. The role also touches upon agent memory, suggesting a secondary stage involvement.

Applied Scientist II role focused on detecting sophisticated invalid traffic (IVT) in advertising using deep learning, self-supervised techniques, representation learning, and advanced clustering. The role involves defining research problems, inventing ML approaches, designing and deploying production-quality ML components, and working with massive datasets. It also requires producing research reports and contributing to the scientific community through publications.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery of solutions impacting international customers.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology.

This internship focuses on research in Reinforcement Learning and Optimization within Machine Learning, developing and implementing novel algorithms for complex real-world challenges. The role involves working with large-scale data and applying cutting-edge ML techniques.

This internship focuses on developing systems and frameworks for machine learning asset lifecycle management, leveraging NLP and information retrieval. The role involves research into ML operations and knowledge engineering to enhance Amazon's ML capabilities.

Research Scientist role focused on applying and extending state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning, and large language models to improve product compliance and safety at Amazon. The role involves researching and evaluating algorithms, designing new algorithms for business impact (e.g., synthetic data generation, active learning, grounding LLMs), and collaborating with engineering and product teams to implement ML solutions across the product catalog. The team specializes in image and document understanding for compliance capabilities, with a focus on publishing research.

Senior Applied Scientist role at AWS Healthcare AI, focusing on developing and researching AI-driven clinical solutions to transform healthcare delivery. The role involves defining research directions, developing new ML techniques, and ensuring research translates into impactful products for clinicians and patients. Requires a PhD or Master's with significant experience in ML, NLU, deep learning, foundation models, and RL, with a strong publication record.

This role focuses on developing and launching end-to-end AI solutions for Prime Video's recommendation and personalization systems. It involves deep learning, GenAI, reinforcement learning, and optimization methods, with a strong emphasis on experimental design (A/B testing) and research publication. The scientist will work closely with engineers and product managers to bring these solutions to millions of customers.

Research scientist role focused on developing novel machine learning solutions for music and podcast recommendations within Amazon Music. The role involves implementing and validating ideas through A/B testing, producing innovative research for peer-reviewed publications, and building scalable models. It requires a PhD or Master's degree and experience in deep learning, ML, or NLP, with a focus on recommender systems.

This role leads teams of applied scientists and ML engineers to develop and deliver AI systems for Amazon businesses, focusing on integrating information into AI systems using techniques like RAG. The role involves defining technical roadmaps, mentoring teams, and driving research from conception to production, with a strong emphasis on building impactful AI-driven products and services.

Senior Applied Scientist role focused on research, design, and development of new AI technologies for personalization, including recommendation systems and large language models. The role involves inventing, experimenting with, and launching new features, products, and systems that impact millions of customers.

Research Scientist role focused on developing and launching new AI technologies for personalization, leveraging large datasets and computational resources to build large-scale machine learning solutions for customer recommendations. The role involves inventing, experimenting with, and launching new features, products, and systems, with a strong emphasis on research publications.

This internship focuses on bridging cutting-edge AI research with practical application and communication. The intern will translate complex AI concepts into understandable content for business stakeholders and the wider community, document AI capabilities, develop internal AI literacy programs, and contribute to applied research projects in NLP, Computer Vision, or Multimodal AI. The role requires a strong foundation in ML/DL, Python, and ML frameworks, with a passion for science communication and a curious, open mindset.

Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research.

Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research.

The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research.

Research Scientist role focused on building and deploying large-scale foundation models for demand forecasting at Amazon. The role involves designing experiments, developing deep learning and statistical models, and analyzing large datasets to improve forecasting accuracy and downstream business impact. Emphasis on research rigor, production deployment, and scientific contribution.

This role focuses on developing AI and ML solutions for last mile delivery automation, combining expertise in machine learning, computer vision, and robotics to solve complex challenges in perception, navigation, and path planning. The scientist will research, design, and implement algorithms, transforming research concepts into production-ready solutions for autonomous systems.

This role focuses on researching and developing advanced navigation systems for intelligent robotic products, utilizing a spectrum of approaches from classical methods to learning-based techniques and foundation models. The primary goal is to enable robots to move reliably and safely in complex, dynamic environments, with a strong emphasis on sim-to-real transfer and evaluation frameworks.

Applied Scientist II at Amazon Alexa AI focused on prototyping, optimizing, and deploying ML algorithms in Generative AI. Responsibilities include research, building PoCs, collaborating with teams, technical communication, documentation, and publishing research.

This role focuses on building AI/ML solutions for the Ad Sales business, specifically creating customer-facing recommendations and enhancing end-to-end workflows with Generative AI. The scientist will leverage quantitative modeling techniques like Sequential Recommender Systems, Deep Learning, and Reinforcement Learning, and use NLP and Generative AI for explainability. The role involves research, model development, A/B testing, and collaboration with engineering and product teams to deliver production-ready solutions.