Amazon

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.

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.

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 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.

PhD intern/co-op role in Amazon's Personal Robotics Group focusing on research and development of intelligent robotic products, including Amazon Astro. The role involves working on the full spectrum of robotics, from hardware design to software and control systems, with a focus on manipulation, locomotion, and human-robot interaction. Requires a PhD in a relevant field and experience in programming languages like Python, C++, or Java.

Applied Scientist II role focused on designing, developing, and deploying Generative AI and LLM solutions for Amazon. The role involves working with foundation models, prompt engineering, RAG, fine-tuning, and production deployment of AI systems, with a focus on applied research and evaluation.

This role is for a Senior Software Development Manager leading a team to build AI agents and tools that simplify and accelerate customer adoption of AWS Neuron, a software stack for Amazon's Machine Learning silicon (Trainium). The focus is on applying Generative AI to improve the process of porting and optimizing ML workloads on Neuron, involving collaboration with scientists, engineers, and customers.

This role focuses on developing training environments, tasks, and integrations for scaling RL environments and core model capabilities for browser-based agents. The primary responsibility is to architect and deliver robust software solutions, including agentic harnesses, and engineer high-performance systems using TypeScript and Python.

Senior Machine Learning Engineer at AWS Applied AI Solutions focused on building a new agentic product. The role involves transforming research into production systems, owning end-to-end deployment of Generative AI and ML methods, and establishing scalable processes for model development, validation, and serving. Requires expertise in agentic systems, production ML, and scalable deployment architectures, bridging research and customer-facing products.

Software Development Manager for Amazon's Devices & Services Trust CX Innovations team, focusing on building and scaling teams that deliver privacy-first, accessible, and trustworthy AI experiences for consumer devices like Alexa and Echo. The role involves driving technical strategy for privacy-preserving AI architectures, responsible AI frameworks, and accessibility features, while balancing performance with privacy, building explainable AI systems, and creating guardrails for LLMs. Key challenges include latency vs. privacy trade-offs, AI safety at scale, ambient computing privacy, multimodal AI systems, and real-time evaluation.

Applied Scientist role focused on developing AI/ML solutions for Last Mile Delivery Automation, combining expertise in machine learning, computer vision, and robotics for perception, navigation, and path planning. The role involves transforming research into production-ready solutions and collaborating with engineering teams.

Senior Applied Scientist at Amazon Ads focusing on applying cutting-edge generative AI and LLMs to the advertising life cycle. The role involves researching, developing, and deploying ML solutions for ranking, personalization, NLP, computer vision, recommender systems, and LLMs. It requires driving end-to-end projects, building and optimizing models, running A/B experiments, and developing scalable ML processes. The role emphasizes impacting millions of customers and advertisers through innovative ML solutions at massive scale.

Software Development Engineer focused on optimizing and enabling deep learning and GenAI workloads, specifically LLMs, on AWS's custom ML accelerators (Neuron SDK, Inferentia, Trainium). The role involves system-level and low-level optimizations for inference performance, working across frameworks, kernels, and hardware boundaries.

Senior Software Development Engineer for AWS Neuron, focusing on accelerating deep learning and GenAI workloads on custom ML accelerators (Inferentia and Trainium). The role involves optimizing inference performance for LLMs, working across the stack from frameworks to hardware-software boundaries, and collaborating with compiler, runtime, and hardware teams. Key responsibilities include designing, developing, and optimizing ML models and frameworks, building infrastructure for model onboarding, implementing low-level optimizations, and working with customers on model enablement.

Applied Scientist role focused on enhancing AWS software stack for Trainium and Inferentia accelerators, involving ML/RL for kernel/code generation, ML compiler techniques, system robustness, and efficient kernel development. Collaborates with customers and engineering teams to optimize ML systems and adoption.

This role focuses on developing next-generation advanced robotics systems by combining AI, control systems, and mechanical design for automation at Amazon's scale. The intern will contribute to research bridging theoretical advancements and practical implementation in robotics, focusing on areas like dexterous manipulation, locomotion, and human-robot interaction, leveraging deep learning and LLMs.

Principal Applied Scientist role focused on developing and deploying generative AI solutions for Amazon's Sponsored Products and Brands advertising platform. The role involves defining science vision, building ML/LLM models for advertiser and shopper experiences, optimizing campaign performance, and leading scientific rigor. Requires strong ML, LLM, and GenAI expertise with experience in production systems and digital advertising.

Software Development Engineer focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium), working across the stack from frameworks like PyTorch/JAX to hardware-specific optimizations and kernel development.

Senior Manager role leading a team of Applied Scientists and Engineers to develop and deploy generative AI solutions for Amazon's Sponsored Products and Brands advertising platform, focusing on multi-lingual and multi-modal applications to drive growth in non-US markets.

Machine Learning Systems Engineer for Frontier AI Robotics team, focusing on building and optimizing distributed training infrastructure for large-scale deep learning and transformer models. Role involves engineering scalable, high-performance systems for AI research and applications, with a focus on robotics, multimodal perception, and manipulation strategies. Requires strong software development, ML infrastructure, and deep learning framework expertise.

Lead a team to invent and build the SPB-Agent, a GenAI platform transforming retail-media advertising for Amazon advertisers. This agent will act as an intelligent advisor integrated into Amazon Ad Console and Seller/Vendor portals, using conversational interfaces and deep reasoning to help advertisers discover growth opportunities, optimize campaigns, and execute strategies at scale.

The AWS Neuron Science Team is seeking scientists to enhance their software stack for ML accelerators (Trainium and Inferentia). The role involves working with customers to identify adoption barriers, collaborating with engineering teams on solutions, and advancing ML systems. Key areas include AI for Systems (kernel/code generation), ML Compiler techniques, System Robustness, and Efficient Kernel Development.

Senior Software Engineer role focused on optimizing and deploying large AI models (LLMs, vision generative AI) on AWS's custom AI accelerators (Inferentia, Trainium). The role involves architecting distributed inference solutions, optimizing performance from high-level frameworks to hardware implementations, and developing tools for LLM accuracy and efficiency. It bridges ML frameworks (PyTorch, JAX) with AI hardware, focusing on inference performance and scaling.

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.

This role leads the development of generative AI applications for advertisers, focusing on agentic experiences for recommendations and guidance. It involves fine-tuning, reinforcement learning, and preference optimization, with a strong emphasis on creating customer-facing products and mentoring AI talent.

Software Engineer role focused on building and tuning distributed training solutions for AWS Inferentia and Trainium accelerators, specifically for large language models and other ML model families. The role involves working with PyTorch, Jax, XLA, and the Neuron compiler/runtime to maximize performance and efficiency on AWS Trainium.

Senior Software Development Engineer focused on ML Data Infrastructure for Frontier AI Robotics at Amazon. The role involves building and maintaining scalable data infrastructure, designing dataset management systems, developing visualization tools, and implementing advanced data filtering techniques to support cutting-edge AI robotics research. Collaboration with science teams is key, requiring both infrastructure development and hands-on technical contribution to data preparation.

This role is for a PhD student intern/co-op focused on robotics research, specifically in areas like manipulation, perception, motion planning, and autonomous mobile robots. The role involves applying machine learning, computer vision, and potentially LLMs to solve real-world robotics problems, with a focus on developing research prototypes and seeing them through from concept to working prototype. The work touches on data collection/preparation for training and research.

This role focuses on building next-generation models for intelligent automation within AWS Agentic AI. The scientist will develop innovative solutions for complex problems, focusing on areas like autonomous agents, API orchestration, planning, large multimodal models (especially vision-language), reinforcement learning, and sequential decision making. The role involves partnering with technology and business teams, utilizing extensive data and computational resources, and collaborating with engineers. There's an expectation to publish findings at peer-reviewed conferences.

Senior Software Development Engineer role focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium) through the Neuron SDK. The role involves system-level optimizations, performance tuning for latency and throughput, building infrastructure for model onboarding, and collaborating across hardware, software, and framework teams to ensure optimal performance for customers running large language models and other GenAI workloads.

Lead a team of scientists to research and prototype Machine Learning applications, focusing on Agentic AI and LLM solutions for seller experience, trust, and safety. The role involves designing and implementing large-scale, end-to-end business solutions and influencing technical strategy.

Software Development Engineer to lead and architect next-generation model serving infrastructure for generative AI applications on AWS Inferentia and Trainium accelerators, focusing on performance, reliability, and scalability of inference serving systems.

Senior Applied Scientist role focused on developing and deploying machine learning models for Amazon's autonomous delivery systems, specifically in perception, prediction, and decision-making for autonomous vehicles. The role involves designing algorithms, leading research, transforming concepts into production, creating evaluation frameworks, and collaborating with engineering teams.

The Annapurna Labs team at AWS is seeking an Engineering Manager to lead a team focused on optimizing ML kernel performance for AWS Neuron, their custom ML accelerators (Inferentia and Trainium). The role involves designing and implementing high-performance kernels, optimizing compiler and runtime performance, and working closely with customers to enable their ML models. This position operates at the hardware-software boundary, combining deep hardware knowledge with ML expertise to accelerate deep learning and GenAI workloads.

Software Development Engineer focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium), working across the stack from frameworks like PyTorch/JAX to hardware-specific optimizations and kernel development.

Software Engineer II role focused on optimizing LLM inference performance on AWS custom ML accelerators (Inferentia and Trainium) using the AWS Neuron SDK. This involves developing and tuning ML models and frameworks, building infrastructure for model onboarding, implementing low-level optimizations, and collaborating across hardware, software, and ML teams to ensure peak performance for customers.

Senior Software Development Engineer role focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium). The role involves working across the stack from frameworks (PyTorch, JAX) to hardware, building infrastructure, optimizing performance (latency and throughput), and collaborating with various teams and customers to ensure efficient execution of large language models and other GenAI workloads. Experience with inference serving platforms like vLLM is required.

Seeking a Simulation Engineer to join an AI robotics research team, focusing on developing 3D physics-based simulation frameworks and tools to enable large-scale machine learning model training for robotics. The role involves developing simulations for reinforcement learning, closed-loop simulations, synthetic data generation, implementing robotics features, building real-to-sim workflows, and collaborating with ML researchers.