Principal Applied Scientist role at Prime Video focused on inventing, developing, and deploying AI solutions for personalization and discovery. The role involves technical and strategic leadership, guiding ML systems from research to production, and mentoring scientists. Key responsibilities include prototyping and productionizing large-scale AI solutions using deep learning, generative AI, RL, and optimization, providing technical leadership, designing A/B tests, driving technical bar-raising, and staying ahead of industry trends. The team focuses on creating a highly personalized content discovery experience using ML and Generative AI.

Senior Machine Learning Engineer at AWS Identity Analytics Platform, focusing on building an AI-driven analytics platform that processes petabyte-scale data to generate insights for security and operational problems. The role involves designing, developing, and deploying ML solutions, including anomaly detection, time-series forecasting, classification, optimization models, and LLM-powered agents for conversational data querying. It also includes feature engineering, production deployment, and collaboration with leadership and service teams.

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.

Senior Applied AI Solutions Architect for Amazon Connect, focused on accelerating customer adoption of AI capabilities. The role involves guiding customers in model selection, prompt configuration, and tool integration for AI agents, with a strong emphasis on customer data readiness and enabling multi-agent orchestration. This is a hands-on role requiring coding, integration building, and pair-programming with customer teams to move from proof-of-concept to production.

Senior ML Engineer to build and scale ML systems for intelligent robots, focusing on designing and maintaining infrastructure for training, evaluating, and deploying ML models. The role involves working at the intersection of ML and systems engineering to ensure robust, efficient, and scalable systems, with a focus on optimizing model inference for edge devices.

Senior AI Architect role focused on designing and building AI agents for AWS Professional Services to automate and accelerate consulting services delivery. The role involves working with customers, leading technical solutions, and architecting scalable AI/ML and GenAI solutions on AWS.

Manager for a team of applied scientists and ML engineers focused on building generative AI solutions for advertisers within Amazon Advertising. The role involves setting scientific strategy, mentoring scientists, managing talent, and delivering AI products at scale, with a focus on multi-modal generative AI for creative assets.

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 developing AI and ML systems for industrial robotics and manufacturing, involving real-time decision making, optimization, and inventing new algorithms. The role requires delivering complex solutions into production and has a strong emphasis on scientific code development and evaluation.

The Data Scientist will focus on developing evaluation and benchmarking datasets for generative AI capabilities within the Amazon Quick Suite enterprise AI platform. This includes leveraging LLMs for synthetic data generation, creating ground truth datasets, leading human annotation initiatives, and contributing to Responsible AI efforts to ensure enterprise-readiness, safety, and effectiveness of AI at scale.

Machine Learning Engineer for Alexa AI focused on LLM training, production deployment, and inference optimizations. Will collaborate with Applied Scientists and other MLEs to leverage Amazon's data and computing resources for Generative AI solutions. Responsibilities include investigating design approaches, prototyping, evaluating technical feasibility, processing data, scaling ML models, and delivering high-quality software in an Agile environment. Experience with PyTorch/JAX, vLLM, SGLang, TensorRT, and developing large model hosting platforms is preferred.

Applied Scientist role focused on designing, developing, and deploying machine learning and computer vision models for Amazon's in-store grocery technologies. The role involves end-to-end model development, from ideation to production, with a focus on solving complex grocery-domain problems at scale and improving the customer shopping experience.

Product Manager for an AI-powered infrastructure reliability platform that uses LLMs and multi-agent systems to prevent, detect, and resolve incidents in Amazon's fulfillment network. The role involves defining product roadmaps, writing code for proof-of-concepts, and collaborating with data scientists and engineers on ML model applications, agent architecture, and evaluation frameworks.

Senior Manager to lead an AI Red Team focused on security research and offensive operations targeting AI systems, infrastructure, and emerging threats. The role involves building and leading a team, establishing the AI offensive security research program, driving Red Team operations, and partnering with stakeholders to protect AI offerings and customer trust.

Software Development Manager to lead a team building agentic AI systems for AWS Glue, EMR, and Athena, focusing on automated Spark upgrade and migration agents, and a managed analytics service providing AI assistants and agents access to tools. The role involves owning design, implementation, testing, and deployment, driving technical decisions at the intersection of GenAI, distributed systems, big data, and ML, and partnering with senior leadership and customers.

Manager for an Applied Science team focused on Brand Protection ML at Amazon. The role involves leading scientists to build and launch scalable AI/ML/LLM/GenAI solutions to identify and prevent infringement and counterfeit on Amazon's platform globally. The team works on complex business problems with significant customer impact, leveraging SOTA ML techniques and deep learning, computer vision, and NLP.

Software Engineer II role focused on optimizing and enabling deep learning and GenAI workloads on AWS custom ML accelerators (Inferentia and Trainium) by developing and enhancing the AWS Neuron SDK. This involves working across the stack from frameworks like PyTorch/JAX to hardware-software boundaries, optimizing ML compilers, runtimes, and high-performance kernels for inference and training. The role requires strong software development skills in Python/C++, system-level programming, ML knowledge, and collaboration with various teams to ensure optimal performance for customers.

This role is for a Principal GenAI Specialist SA at Amazon, focusing on designing and architecting scalable, secure, and cost-effective AI/ML, Generative AI, and Agentic AI solutions on AWS. The role involves guiding customers through their AI transformation, establishing GenAIOps practices, and creating enterprise-grade AI architectures. It requires deep technical experience across the AI spectrum, including LLM customization/fine-tuning, inference optimization, agentic frameworks, GenAIOps, security, RAG systems, and prompt engineering.

Applied Scientist role focused on building and deploying Generative AI solutions for Brand Protection using NLP, computer vision, and LLMs. The role involves end-to-end ownership from conception to launch, collaborating with product and engineering teams, and analyzing data to solve complex business problems at scale.

Applied Scientist role focused on developing and deploying production-ready AI/ML models for consumer-facing features like content understanding, recommendations, and GenAI applications. The role involves inventing new approaches, adapting existing ones, and building scalable, efficient solutions. It requires collaboration with scientists and engineers, with a focus on both scientific and engineering best practices, and potentially contributing to research papers. The role touches on inference infrastructure and model serving, with a primary focus on building agentic or product-level AI features.

The Applied Scientist II will focus on building industry-leading Conversational AI Systems using Generative AI, LLMs, NLU, and Applied ML. The role involves developing novel algorithms and modeling techniques to advance human language technology, impacting millions of customers through products and services. The team explores new technologies and finds creative solutions for AWS customers, working with foundation models and generative AI to reimagine customer experiences.

Software Development Engineer II role focused on building and optimizing GenAI serving systems and ML platforms at massive scale. The role involves working with LLMs, VLMs, and multimodal foundation models, including optimized model serving, distillation, quantization, distributed inference, vector indices, and agentic systems. The primary focus is on the engineering and infrastructure aspects of bringing AI models to production, with a secondary involvement in agentic systems.

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 Applied Scientist role focused on building agentic AI products for businesses, involving end-to-end GenAI project ownership, ML model development and deployment, and research into innovative ML approaches. The role emphasizes building multi-agent systems using techniques like fine-tuning and reinforcement learning, with a focus on customer-facing features and scalable solutions.

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.

Machine Learning Scientist role focused on Generative AI within AWS, aiming to identify customer needs and improve cloud adoption. The role involves building Agentic AI systems, fine-tuning LLMs, applying Reinforcement Learning, and generating insights from large datasets, with a focus on taking ideas from conception to production.

Solutions Architect for AWS Telco customers, focusing on customer experience transformation using AI agents, conversational AI, and omnichannel orchestration. The role involves designing and building solutions with AWS services like Amazon Connect and Bedrock AgentCore, driving adoption, and influencing product roadmaps.

Senior Software Development Engineer for AWS Neuron, focusing on accelerating deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). The role involves designing, developing, and optimizing ML models and frameworks for deployment, with a strong emphasis on distributed inference, performance tuning (latency and throughput), and system-level optimizations for LLMs.

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.

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.

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 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.

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.

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.