Senior Applied Scientist role focused on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems, with an emphasis on multi-lingual applications across text, speech, and vision domains. The role involves driving scientific strategy, influencing partner teams, and delivering solutions impacting global customers.

This role focuses on foundational research and building intelligent robotic systems, operating at the intersection of AI research and robotics. The individual will conduct original research, publish findings, and deploy innovations into production systems at Amazon scale. Key responsibilities include driving research initiatives across the robotics stack, designing novel deep learning architectures, guiding technical direction for full-stack robotics projects, and collaborating with platform and hardware teams. The role emphasizes developing breakthrough foundation models and full-stack robotics systems that enable robots to perceive, understand, and interact with the world.

Leads an ML infrastructure team focused on creating model training and simulation environments for large robotics foundation models. This involves defining roadmaps, building realistic simulation environments for RL and synthetic data generation, and implementing tooling for data creation and experimentation. The role emphasizes large-scale training, multi-modal models, and robotics applications.

ML Engineer role focused on building and optimizing distributed training infrastructure for large-scale deep learning and transformer-based models, specifically for frontier AI robotics applications. The role involves working with scientists and engineers to deliver scalable, high-performance systems, leveraging PyTorch, Python, and C++, and optimizing GPU performance for training.

Pioneering next-generation neuro-symbolic tools by fusing AI breakthroughs with cloud scale and automated reasoning expertise. This role involves building scalable formal reasoning solutions, integrating GenAI and Agentic AI, and applying software engineering best practices to production systems. Responsibilities include defining and implementing automated reasoning features, designing and running RL pipelines, experimenting with model tradeoffs, and collaborating cross-functionally. The role also focuses on enhancing formal reasoning systems for GenAI applications, owning the science lifecycle, and advancing the state of the art through publications and patents.

This role focuses on developing and researching agentic AI systems for operational decision-making and orchestration within Amazon's fulfillment network. It involves building full agentic systems using multi-agent orchestration, tool use, memory, and action execution, training LLMs through various methods including RL, and conducting rigorous evaluations. The role also includes leading research projects, mentoring, and publishing academic papers.

Applied Scientist role focused on developing novel AI Agent architectures and multi-modal Generative AI models (audio, images, videos) for advertisers within Amazon Advertising's CreativeX team. The role involves research, development, and productionization of these models, with an emphasis on agent evaluation, LLM/VLM fine-tuning, and reinforcement learning.

This role focuses on designing, developing, and deploying intelligent navigation systems for advanced robotic systems. It involves leveraging machine learning, AI, and control theory to create scalable and safe navigation solutions for dynamic environments. The role bridges research and production, with a strong emphasis on learning-based approaches, foundation models for embodied agents, and control-theoretic methods like MPC. Key responsibilities include developing perception algorithms, leading research in computer vision and sensor fusion, and owning ML models end-to-end, from data to deployment. The role also involves publishing research and mentoring junior scientists.

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.

Senior Applied Scientist focused on designing, developing, and deploying intelligent navigation systems for advanced robotic systems. This role involves leading research in learning-based planning and control, foundation models for embodied agents, and control-theoretic approaches like MPC, with a strong emphasis on translating research into deployed, scalable systems.

Senior Applied Scientist role focused on designing, developing, and evaluating long-running AI agents for AWS Applied AI Solutions. The role involves building agentic use cases, defining evaluation frameworks for complex agent outputs, and ensuring production deployment. Requires experience in building ML models for business applications and applied research.

Senior Applied Scientist at Amazon focused on using Generative AI, VLMs, and multimodal reasoning to understand product identity and relationships within Amazon's catalog. The role involves formulating research problems, designing and implementing models for product relationship inference and catalog understanding, pioneering explainable AI, owning ML pipelines from research to production, defining research roadmaps, and mentoring peers. It emphasizes tackling ambiguous problems at scale, reasoning across text and images, and deploying solutions that impact millions of customers.

Data Scientist role focused on building and shipping multi-agent AI systems for Amazon sellers, involving reasoning, planning, memory, and context engineering. The role requires defining product vision, translating research into features, and designing evaluation frameworks for agent quality and business impact.

Software Engineer role focused on optimizing collective operations for AWS Trainium, a purpose-built AI training chip. The role involves enhancing collective algorithms and topologies, optimizing compute for specific LLM training topologies, and working closely with hardware teams to maximize performance using C/C++. The goal is to scale AI compute across the data center for training frontier AI models.

Research scientist role focused on building neuro-symbolic AI systems using proof assistants for complex problem-solving across various domains within Amazon. The role involves defining and implementing new applications, delivering scientific artifacts, and working in an agile environment. Requires a PhD or Master's with significant applied research experience, and experience leading scientists.

Senior Applied Scientist role focused on developing and deploying state-of-the-art perception algorithms for advanced robotic systems. The role involves research in computer vision, sensor fusion, and 3D perception, with a strong emphasis on bridging theoretical research with real-world impact. Responsibilities include end-to-end ownership of ML models, from data to deployment, and publishing research findings. The role operates at the intersection of deep learning, LLMs, and robotics, aiming to enable seamless interaction between users, robots, and their environment.

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.

Data Scientist role focused on building and shipping multi-agent AI systems for Amazon sellers, involving reasoning, planning, memory, and context engineering. The role requires defining product vision, translating research into features, and designing evaluation frameworks for agent quality and business impact.

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.

Principal Technical Product Manager to define and drive product strategy for training software on AWS Trainium, including distributed training libraries, post-training workflows (RLHF, DPO, fine-tuning), reinforcement learning frameworks, and training performance optimization. The role focuses on enabling researchers and operators to train frontier models at scale.

This role focuses on leading research and development for next-generation proactive and autonomous agentic experiences within Alexa AI. The Principal Applied Scientist will guide a team in leveraging state-of-the-art ML, NLP, LLM training, and agentic AI systems to advance autonomous intelligence and proactive user assistance. Key responsibilities include identifying research directions, developing novel agent solutions, translating research into production, and influencing partner teams to launch AI-powered autonomous agents. The role aims to transform user interaction with Alexa by enabling proactive anticipation of needs, autonomous task execution, intelligent reasoning, continuous learning, and seamless coordination across domains.

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.

Senior Applied Scientist role focused on building agentic AI systems, multi-agent architectures, tool-augmented LLMs, and RAG pipelines for climate-related products. The role involves end-to-end product development from research to production, with a focus on autonomous analysis, planning, and execution of recommendations, leveraging multimodal AI and deep learning on time series data.

This role focuses on building AI safety systems for Alexa, ensuring LLMs provide safe and trustworthy responses. It involves pioneering solutions in Responsible AI, designing automated testing systems, creating intelligent evaluation systems, building models that understand human values, and crafting AI agents for real-time detection and fixing of production issues. The role emphasizes frontier research with immediate real-world impact, aiming to set industry standards for responsible AI.

Research and develop novel approaches for agent-data interaction using generative AI and agentic systems to provide instant, strategic advice to advertisers. Focus on agent orchestration, context optimization, code generation, and RAG-based embeddings for real-time data access with minimal latency and token consumption. Balances applied research (60%) with productionization (40%).

Research scientist role focused on applying Generative AI, VLMs, and multimodal reasoning to product catalog understanding and agentic shopping experiences. The role involves formulating research problems, pushing boundaries of foundation models, advancing efficient model deployment, and ensuring reliability through interpretability and uncertainty calibration. It spans the full research lifecycle from problem formulation to production deployment, with a strong emphasis on publishing findings and mentoring.

Senior Applied Scientist role focused on advancing conversational AI technologies, specifically LLMs and generative AI, for Alexa. The role involves defining science roadmaps, architecting agentic systems, establishing evaluation frameworks, and driving end-to-end delivery of research initiatives from experimentation to production. Emphasis on building scalable agentic systems for conversation understanding and generation, and contributing to the team's scientific reputation through publications and patents.

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.

Research scientist role focused on designing and building large-scale foundation models for time series demand forecasting. The role involves developing novel architectures, training strategies, and data generation techniques, with a strong emphasis on both scientific research (publications) and production deployment impacting millions of dollars in automated decisions. Experience with transfer learning, zero-shot forecasting, and synthetic data generation is key.

This role focuses on designing and developing generative AI solutions, specifically large language models and multimodal AI, for real-time ad allocation and ranking in a high-volume consumer advertising system. It involves research into semantic relationships, dynamic optimization, and integration into existing systems, with a strong emphasis on efficiency and strict latency requirements.

Senior Applied Scientist role focused on shaping visual shopping experiences using agentic AI, multimodal personalization, and real-time image/video generation. The role involves defining scientific vision, architecting multimodal understanding and generation systems, and establishing evaluation frameworks for visual agentic experiences. It requires strong research skills, practical engineering instincts, and influencing cross-functional teams, with a focus on delivering scalable solutions and contributing to publications/patents.

Seeking Principal Engineers to join AWS Agentic AI organization. This role involves designing, building, and scaling systems for AI agent platforms, services, and tools, focusing on multi-agent workflows and foundational technical infrastructure. The position requires strong technical leadership, architectural design, and the ability to drive innovation in a fast-paced environment.

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 Scientist role focused on building and deploying production-grade, multi-agent generative AI systems for Amazon's Seller Assistant, impacting millions of sellers worldwide. The role involves creating next-generation tools, designing and deploying innovative models, and establishing scalable processes for model implementation and validation.

Applied Scientist role focused on leveraging Generative AI, VLMs, and multimodal reasoning to solve complex product identity and relationship inference problems at Amazon's scale. The role involves pioneering advanced GenAI solutions for next-generation agentic shopping experiences, working with massive multimodal data, and deploying algorithmic ideas at scale. Responsibilities include formulating research problems, designing and implementing models, pioneering explainable AI, owning ML pipelines, defining research roadmaps, and mentoring peers.

This role focuses on developing novel frameworks and techniques for multi-object tracking, re-identification, person activity understanding, and multi-modal foundation models within the context of Amazon's Just Walk Out technology. The scientist will advance the theory and practice of these areas, create efficient visual processing techniques, and reduce computational/data requirements for visual AI systems. The role requires a strong publication record in top-tier conferences and experience in computer vision, deep learning, and multi-modal foundation models.

Lead a new applied science organization focused on building agentic AI systems for advertising campaigns. This role involves defining the scientific vision, research agenda, model architectures, and evaluation frameworks for LLM-based agents, multi-step planning, tool use, RAG, and RLHF, with a focus on delivering measurable value and transforming the advertiser journey. The role requires building and mentoring a team, partnering with cross-functional teams, and driving execution from research to production at scale.

Lead product vision and roadmap for Amazon Seller Assistant, a GenAI-first, multi-agent system. Responsible for defining innovations and scaling agentic capabilities for millions of sellers, collaborating with scientists and engineers to launch production-grade multi-agent systems at Amazon's scale. Also responsible for building and scaling industry-leading evaluation infrastructure.

This role is focused on developing foundational capabilities for AI agents, combining LLMs with RL for reasoning, planning, and world modeling in virtual and physical environments. The role involves maintaining a task management system to support data and reliability improvements, with a focus on building the agent system from the ground up.

Senior Principal Scientist role focused on developing industry-leading Agentic AI solutions for AWS, including autonomous agents for incident detection/resolution and proactive code repair, leveraging advanced deep learning and foundation models for cloud observability and security.

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.

Senior Software Engineer role focused on developing, enabling, and optimizing large-scale ML model training (pre-training and post-training) on AWS Trainium accelerators. This involves working with distributed training frameworks, mixed-precision techniques, and performance tuning across various model families including LLMs, multimodal models, and RL workloads.

Research role focused on developing foundational capabilities for AI agents, combining LLMs with reinforcement learning for reasoning, planning, and world modeling in virtual and physical environments. Involves model training, dataset design, and pre/post-training optimization.

This role focuses on developing and implementing advanced visual reasoning systems and autonomous AI agents that understand complex spatial relationships, object interactions, and customer behavior patterns in real-time retail environments. It involves working at the intersection of computer vision and large language models to advance state-of-the-art visual AI.

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.

Senior Applied Scientist role focused on AI research and development, including Generative AI, Agentic AI, LLMs, and Diffusion Models for Amazon's catalog systems. The role involves designing, training, and deploying AI solutions, with a focus on scaling models and integrating them into production.