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.

Research scientist role focused on building next-generation models for intelligent automation using autonomous agents, API orchestration, multimodal models, and reinforcement learning within AWS.

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.

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 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.

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.

Research-focused Applied Scientist role at Amazon working on generative AI for Alexa, focusing on LLMs, multimodal models, AI safety, alignment, and responsible AI. The role involves developing innovative solutions, optimizing models, evaluating performance, and leading the development of production-ready AI solutions, with a strong emphasis on research publications and patents.

Applied Scientist II at Amazon Alexa International focusing on developing and applying LLMs and multimodal systems for multi-lingual applications. The role involves research, fine-tuning/post-training LLMs, building evaluation metrics, and driving scientific strategy from research to production, impacting global customers.

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 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.

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%).

Applied Scientist role focused on building and evaluating LLMs and multimodal systems for Alexa Connections, involving fine-tuning, post-training, and contributing to research and production delivery.

Research scientist role focused on combining LLMs with classical AI reasoning for robotics and automation applications. The role involves generating plans, verifying correctness, learning strategies, and self-improving models, with an emphasis on publishing research and applying technology to operational problems.

Research scientist role focused on combining LLMs with classical AI reasoning for robotics and automation applications. The role involves generating plans, verifying correctness, learning strategies, and self-improving models, with an emphasis on publishing research and applying technology to operational problems.

Senior Applied Scientist role focused on Generative AI, LLMs, and multimodal models for Alexa+, emphasizing AI trust, privacy, safety, and alignment. The role involves leading research, developing optimization techniques, pioneering responsible AI methods, and collaborating with product and engineering teams to deliver production-ready AI solutions.

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.

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.

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 advancing the state of the art with LLMs and multimodal systems for Alexa's international products. The scientist will develop novel algorithms, build evaluation metrics, fine-tune/post-train LLMs using advanced techniques (SFT, DPO, RLHF, RLAIF), and contribute to industry-first research. The role involves end-to-end delivery from research to production, influencing cross-team scientific strategy, and mentoring junior scientists. Key areas include multi-lingual applications, text, speech, and vision domains, with a strong emphasis on LLM evaluation and post-training methodologies.

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.

This role focuses on building the next generation of models for intelligent automation using autonomous agents, API orchestration, large multimodal models (especially vision-language models), reinforcement learning, and sequential decision making. The role involves developing innovative solutions, publishing findings, and partnering with engineers and developers.

Research and develop novel approaches for agent-data interaction using generative AI and agentic systems, focusing on agent orchestration, context optimization, and code generation for real-time advertiser data at scale. This role involves applied research (60%) and productionization (40%), aiming to improve latency, token consumption, and accuracy.

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.

Applied Scientist role focused on developing novel multi-modal generative AI agentic architectures and models for advertising creatives, integrating and deploying ML projects, curating datasets, and performing analysis. The role involves research, publication, and collaboration with cross-functional teams.

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.

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.

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.

Senior AI/ML researcher to define ML strategy for a healthcare foundation model and inference system, focusing on frontier models, proprietary domain models, and monetizable features under regulatory constraints. Requires expertise in training/adapting large models, distributed training, RLHF/DPO, retrieval, evaluation, and ML systems engineering, with experience in high-stakes/regulated domains.

Internship role focused on developing novel algorithms at the intersection of LLMs and generative AI for robotics, involving research in perception, manipulation, and control. Requires strong ML/DL/robotics background and publication record.

This internship focuses 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. The role involves collaboration with cross-functional teams and requires a strong background in machine learning, deep learning, and/or robotics, with a publication record at top conferences.

This internship focuses on developing novel algorithms at the intersection of LLMs and generative AI for robotics, involving research in robotic perception, manipulation, and control, with an emphasis on multimodal models and vision-language-action 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.

Research intern role focused on developing foundational capabilities for AI agents, combining LLMs with RL for reasoning, planning, and world modeling. The role involves running experiments, building tools to accelerate research workflows, and scaling AI systems within a fast-paced, iterative research lab environment.

Computer Vision Scientist role focused on developing and evaluating generative AI models for e-commerce media content, leveraging large datasets and cloud resources. The role involves research, implementation of novel ML techniques, and communication with stakeholders.

Research role focused on developing foundational capabilities for AI agents that can act in digital and physical worlds, with a focus on multimodal LLMs, automation agent systems, and applying GenAI to real-world problems. Involves rapid invention, experimentation, and collaboration.