Amazon

Data Scientist to build and launch production-grade agentic capabilities for Amazon Seller Assistant, a multi-agent GenAI system. Responsibilities include analyzing seller pain points, designing measurement frameworks, applying NLP and statistical modeling, and collaborating with cross-functional teams to improve the seller experience at Amazon's scale.

Applied Scientist role focused on building LLM-driven conversational assistants for enterprise use cases in hospitality and senior living, leveraging Amazon's scale and data. Responsibilities include developing core LLM technologies, prompt optimization, and building/measuring metrics for these systems.

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.

This role focuses on developing and deploying advanced ML/AI technologies for catalog expansion, including information extraction, website comprehension, and agentic systems for multi-step decision-making. It involves working with large-scale data, deep learning, NLP, and image processing to extract and structure information from various document types, with an emphasis on scalable solutions and leveraging recent advances in RL-based fine-tuning methods.

Senior Applied Scientist role focused on AI-driven structured field robotics, including path planning, fleet coordination, and control systems. The role involves leading research, translating breakthroughs into production solutions at scale, and owning end-to-end delivery of algorithmic solutions. It requires a PhD or Master's with significant experience in robotics, ML, and algorithm development, with a focus on publishing research and mentoring junior scientists. The team operates at the intersection of planning, algorithmic, and ML research with production systems.

Software Development Engineer role focused on building and launching production-grade, multi-agent GenAI systems for Amazon Seller Assistant. The role involves end-to-end ownership from customer insight to shipped product, operating at Amazon's scale.

This role focuses on converting AI ambition into programs that can be delivered, operated, and scaled in production environments. The AI Specialist SA team builds technical relationships with customers of all sizes and operate as their trusted advisor, ensuring they get the most out of the cloud at every stage of their journey while adopting GenAI/ML and Agentic technologies across their organisation. You’ll manage the overall technical relationship between AWS and our customers, making recommendations on security, cost, performance, reliability and operational efficiency to accelerate their challenging GenAI/ML and Agentic projects.

Senior Software Engineer focused on MLOps for speech synthesis and GenAI experiences, involving building and maintaining ML infrastructure for the entire lifecycle on AWS.

Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. Responsibilities include designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating on production deployment.

Senior Applied Scientist role at Amazon focusing on Generative AI for Alexa Conversational Ads and Personalization. Responsibilities include defining scientific vision, leading ML projects, architecting large-scale ML systems, mentoring junior scientists, and collaborating with product/engineering. Requires experience in building ML models for business applications, ML/LLM fundamentals, and large-scale systems. Preferred experience in ad tech and building ML models for recommendations, ads ranking, personalization, or search.

Applied Scientist role focused on leveraging computer vision, generative AI, and deep learning to enhance vehicle navigation and ensure safe, efficient deliveries by analyzing multimodal data. The role involves building large-scale ML systems, translating business requirements into prototypes, and optimizing models for production and edge devices.

The Applied Scientist II role focuses on developing and improving machine learning systems for industrial robotics, specifically leveraging and adapting foundation models for tasks like perception, reasoning, and action. This involves fine-tuning, optimization, experimentation, and building evaluation frameworks, with a contribution to data and training workflows. The goal is to enable generalization, multi-modal learning, and skill acquisition in robots operating at Amazon's scale.

This role focuses on developing and optimizing AI-driven conversational shopping experiences using ML, NLP, and multimodal technologies. The Data Scientist will work on agentic systems, information retrieval, recommender systems, and multimodal LLMs to improve customer journeys, analyze experiments, and collaborate on deploying production systems. The role involves handling large-scale data and contributing to both agent capabilities and the underlying inference infrastructure.

This role focuses on developing advanced machine learning approaches and agentic systems for trust and safety in AWS cloud communication services. The primary goal is to create behavioral detection models and intelligent resource allocation algorithms that adapt to evolving threats and optimize service delivery. The role involves researching novel AI agent applications in security, integrating science components into production, and conducting rigorous experimentation.

The AI Principal PMT for Alexa Responsible AI will define the standard for how Alexa earns and keeps customer trust. This role owns the product discipline of Responsible AI, defining customer experiences for safety guardrails, trust signals, and evaluation frameworks. The PMT will set product vision and strategy, lead cross-functional alignment across Applied Science, Engineering, Legal, Policy, and UX, and ensure the full responsible product experience including safety, privacy, and security. The role requires technical depth in LLMs and AI safety, understanding how models fail and writing requirements for safety model development and evaluation system design. The PMT will also mentor other PMs and influence Responsible AI scaling across Alexa.

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.

Senior Applied Scientist role focused on building state-of-the-art ML models for healthcare challenges within Amazon's Special Projects team. The role emphasizes practical implementation, driving ML advancements, and delivering products to market in an entrepreneurial, startup-like environment. Requires a strong background in AI/ML, leadership skills, and the ability to translate research into actionable plans and practical solutions.

Director of Software Development for Alexa Connections, leading a team to build communication experiences and AI primitives using generative AI, LLMs, voice, and GUI. Focuses on customer-facing UX, communication systems, third-party interfaces, and LLM architecture optimization for Alexa+.

Data Scientist to build and launch production-grade agentic capabilities for Amazon Seller Assistant, a multi-agent GenAI system. Responsibilities include analyzing seller pain points, designing measurement frameworks, applying NLP and statistical modeling, and collaborating with cross-functional teams to improve the seller experience at Amazon's scale.

The role involves developing and scaling a deep learning compiler stack for AWS Machine Learning accelerators (Inferentia and Trainium chips). The engineer will architect and implement features for the AWS Neuron SDK, focusing on making LLM and Vision models run performantly on accelerators. This includes compiler development, optimization, and integration with ML frameworks like PyTorch, TensorFlow, and JAX.

MS or PhD student internship focused on machine learning, deep learning, generative AI, LLMs, speech, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods. The role involves designing and developing end-to-end systems, writing technical white papers, creating roadmaps, and driving production-level projects. Interns will work closely with scientists to develop and deploy solutions, design new algorithms and models, and potentially publish work at top-tier conferences.

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.

Applied Scientist II role focused on building AI-based services for Amazon Q Developer, aiming to redefine developer workflows. The role involves working on ambiguous problem areas, driving the delivery of end-to-end modeling solutions, and collaborating with other AWS AI services. The team builds AI products deployed in IDEs, AWS console, and web tools, providing developers with AI assistants for code generation and AWS interaction.

Software Engineer role focused on enhancing collective algorithms and topologies for optimal AI training performance on Amazon's Trainium chips. This involves optimizing communication primitives to scale AI compute across data centers, working closely with hardware teams, and developing C/C++ implementations for training LLMs.

Principal Data Scientist role at Amazon ProServe, focusing on architecting and implementing enterprise-scale AI/ML and generative AI solutions for customers. Requires technical leadership, strategic advisory, and developing reusable frameworks. Involves customer-facing engagements and mentoring junior data scientists. Requires Top Secret clearance.

Principal Data Scientist role at Amazon ProServe, focusing on architecting and implementing enterprise-scale AI/ML and generative AI solutions for AWS customers. Requires technical leadership, strategic advisory, and driving customer adoption of AWS AI/ML services. Involves leading complex initiatives, translating business challenges into technical solutions, and developing reusable frameworks. Requires a Top Secret security clearance.

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.

Applied Scientist role at Amazon Advertising focused on building LLM-based solutions for advertiser support, predicting problems, and coaching users. The role involves applying NLP techniques, developing scalable ML solutions, and working with AWS services to create customer-facing applications.

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.

Senior Applied Scientist at AWS Healthcare AI focused on developing and researching AI-driven clinical solutions to transform patient-clinician interaction and care documentation. The role involves leading research, developing new ML techniques, and ensuring research translates into impactful products, with a focus on generative AI experiences.

This role focuses on applying AI and machine learning to optimize Amazon's last-mile delivery network. Responsibilities include developing sophisticated ML models for logistics, forecasting, and resource allocation, architecting AI-powered systems, implementing deep learning for image recognition, and developing reinforcement learning for adaptive scheduling. The role also involves designing AI agents for autonomous decision-making and creating models for customer behavior analysis. A strong emphasis is placed on research, publishing findings, and leveraging big data and cloud platforms.

Principal Applied Scientist at AWS Marketplace & Partner Services focused on developing and evaluating next-generation search, recommendation, and agentic systems to drive AWS revenue growth. The role involves defining technical strategy, leading innovations in information retrieval, recommendation systems, LLMs, and agentic AI, and mentoring other scientists. Key responsibilities include architecting agentic AI systems, bridging theory with practice, and contributing to the scientific community.

This role focuses on developing and implementing Agentic AI and automation solutions for identity verification, account support, and compliance within Amazon's Relay product. The engineer will lead the end-to-end development of agentic workflows, integrate Generative AI and LLMs, and enhance existing systems to improve efficiency and reduce waste and abuse.

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.

Senior Applied Scientist role focused on building machine learning and GenAI solutions, specifically agentic frameworks, to improve customer support for Amazon's selling partners. The role involves end-to-end development, collaboration with engineers and product owners, and applying state-of-the-art ML/GenAI techniques to automate workflows and diagnose issues.

This role focuses on building and optimizing the systems and silicon that power AI infrastructure, including custom ML accelerator chips, distributed training systems, and compiler optimizations for ML training. It's an early career role within Annapurna Labs at AWS, aiming to accelerate AI development.

Senior Software Engineer to lead technical vision and innovation for a new team building a horizontal agentic AI layer for Amazon Advertising. The role involves architecting and implementing robust systems using LLMs and autonomous agents to transform advertiser interactions with the platform.

Senior leader to build and lead a team of ProServe Cloud Architects specializing in AI, GenAI, and Agentic AI within the Healthcare and Life Sciences domain. The role involves counseling executives on AI transformation programs, developing repeatable partnership models, and designing scalable AI solutions. Requires expertise in AI/GenAI/Agentic AI within HCLS and experience in business development or professional services.

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.

Applied Scientist II at Amazon Payment Products focused on designing and deploying scalable ML, GenAI, and Agentic AI solutions for financial products. The role involves developing deep learning and LLM models for tasks like automation, text processing, pattern recognition, and anomaly detection, with a strong emphasis on production deployment and iterative improvement.

Applied Scientist role focused on developing and implementing novel foundation models for logistics, involving multimodal data, training at scale, and inference. The role spans from data preparation to model training, evaluation, and inference, with a focus on production environments.

Senior Software Development Engineer role focused on leading the development and implementation of a generative marketing agentic framework (GeMA) at Amazon. The role involves designing a multi-agent architecture, establishing evaluation frameworks, and integrating AI-based solutions for personalized marketing at scale. It requires technical leadership, collaboration with cross-functional teams, and research into LLMs and multi-agent AI systems.

Product Manager - Technical role for an AGI Autonomy Lab focused on developing foundational capabilities for AI agents. The role involves defining and prioritizing tooling roadmaps for data collection, model evaluation, and release processes, bridging research and engineering by translating technical needs into product requirements. Key focus areas include combining LLMs with RL for reasoning, planning, learned world models, and generalizing agents to physical environments.

This role focuses on developing advanced robotics systems that integrate AI, control systems, and mechanical design for automation. The scientist will lead the design and implementation of methods for Visual SLAM, navigation, and spatial reasoning, leveraging simulation and real-world data for model development. The goal is to create a hierarchical system combining low-level control with high-level planning for dexterous manipulation and human-robot interaction.

Software Development Engineer III at Amazon Annapurna Labs, focused on building AI agents and tools to simplify and accelerate customer adoption of AWS Neuron, the software stack for Amazon's ML silicon (Trainium). The role involves technical leadership, research, and delivery of innovative software solutions to improve ML workload porting and optimization on AWS hardware.

Principal Software Engineer for Amazon's Alexa AI organization, focusing on the AI runtime backbone (Aurora). The role involves architecting and delivering large-scale, multi-modal, multi-lingual, and multi-model AI systems, including orchestration, routing, and inference optimization. Responsibilities include building evaluation infrastructure, ensuring responsible AI deployment, and defining technical strategy for AI experiences. This is a senior engineering role focused on production systems at scale.