Research role focused on developing foundational Generative AI (GenAI) technology using Large Language Models (LLMs) and multimodal systems, involving model training, dataset design, and pre/post-training optimization.

Applied Scientist role focused on developing novel algorithms and modeling techniques for LLMs and multimodal systems within Alexa Connections. Responsibilities include analyzing customer behavior, building evaluation metrics, fine-tuning/post-training LLMs, setting up experimentation frameworks, and contributing to end-to-end delivery from research to production, with potential for publications.

PhD internship focused on applied science in Gen AI and LLMs, involving fine-tuning models, developing novel algorithms for NER, recommendation systems, and question answering, and exploring generative AI applications.

PhD internship focused on research in Natural Language Processing (NLP), Natural Language Understanding (NLU), and Speech Technologies, including large language models (LLMs) and reinforcement learning with human feedback (RLHF). The role involves developing and implementing novel algorithms on production-scale data to advance the state-of-the-art.

This role focuses on building AI safety systems for conversational AI, specifically for Alexa. It involves pioneering solutions in Responsible AI, training models for safety standards, designing automated testing for vulnerabilities, creating intelligent evaluation systems, building models to understand human values, and crafting feedback mechanisms. A key aspect is building AI agents for real-time detection and fixing of production issues. The role emphasizes frontier research with real-world impact, focusing on training truthful and grounded models, building reward models for human values, and creating automated systems to discover and address issues. Collaboration with scientists, PMs, and engineers is expected to transform ideas into production systems. The role also involves leading certification processes, advancing optimization techniques, building human-like evaluation systems, and mentoring others.

Principal Applied Scientist to lead science behind Alexa+, Amazon's LLM-powered conversational assistant. Owns technical direction for LLM fine-tuning, alignment, agentic reasoning, and evaluation, impacting hundreds of millions of customers. Defines research directions, designs experiments, ensures translation to production systems, mentors scientists, and represents Amazon in the research community.

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.

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.

Applied Science role focused on developing foundational capabilities for useful AI agents, leveraging large vision language models (VLMs) with reinforcement learning (RL) and world modeling. Responsibilities include model training, dataset design, and pre- and post-training optimization in an applied research setting.

Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems.

Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems.

Seeking an Applied Scientist to develop industry-leading technology with LLMs and multimodal systems, focusing on advanced approaches, model-in-the-loop and human-in-the-loop for high-quality data collection and LLM training, and enhancing customer experiences.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Applied Scientist role at Amazon focusing on improving shopping experiences using LLMs. The role involves post-training of LLMs, including instruction tuning, reward modeling, and reinforcement learning. Responsibilities include designing and running large-scale experiments, analyzing model behavior, and developing new training recipes to enhance capabilities like reasoning and user experience. Requires a PhD or Master's with significant experience, practical LLM experience, and a strong publication record.

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.

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.

The Applied Scientist will work on building industry-leading Conversational AI Systems, focusing on Natural Language Understanding, Dialog Systems, Generative AI with LLMs, and Applied Machine Learning. The role involves developing novel algorithms and modeling techniques to advance human language technology, impacting millions of customers through products and services. The scientist will gain hands-on experience with Amazon's text, structured data, and large-scale computing resources.

Applied Scientist II role focused on developing foundation models for industrial robotics, integrating multi-modal learning, skill acquisition, perception, and environmental understanding. The role involves leveraging, adapting, and optimizing state-of-the-art models, conducting rigorous experimentation, building evaluation benchmarks, and contributing to data and training workflows. It requires strong programming skills in Python and experience in deep learning areas like computer vision, multimodal models, or RL for robotics.

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.

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.

Senior Applied Scientist role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a strong emphasis on research, publication, and deploying these models into production systems for developers.

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.

Data Scientist II role focused on applying statistical, machine learning, and GenAI methodologies to enhance employee experience within Amazon's People Experience and Technology organization. The role involves designing, developing, and maintaining scalable models and prototypes, partnering with cross-functional teams, and creating benchmarks for GenAI model performance.

This role focuses on analyzing and evaluating conversational interaction data to support the training and evaluation of LLMs and machine learning models for Alexa's speech interfaces. The Language Data Scientist will own data analysis, research requests, and contribute to developing annotation workflows and evaluation conventions.

This role focuses on building and evaluating state-of-the-art ML models for biology and life sciences applications, requiring experience with deep learning methods and programming in languages like Python. The role is part of a special projects team aiming to innovate at scale and bring products to market.

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.

This role focuses on developing causal inference models, automated explainability frameworks, and GenAI-powered narrative generation to translate forecasting outputs into actionable business intelligence for Amazon's business customers. The data scientist will build automated variance decomposition models and a causal model library with standardized pipelines, applying techniques from causal inference and time-series econometrics.

This role focuses on improving existing machine learning methodologies within Amazon's supply chain optimization technologies. Responsibilities include analyzing large datasets, developing new data sources, enhancing and testing models, running computational experiments, and fine-tuning model parameters. The role also involves formalizing model assumptions, identifying outliers, and communicating findings to various stakeholders. Collaboration with internal and external researchers, including publishing papers, is expected.