This role focuses on developing and scaling a machine learning compiler for AWS Neuron, which optimizes the performance of neural network models on custom AWS hardware accelerators (Inferentia and Trainium). The engineer will architect and implement features for the compiler stack, which integrates with popular ML frameworks, aiming to improve inference and training performance for large ML workloads.

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.

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.

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 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.

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.

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.

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

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.

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.

This role focuses on building and deploying machine learning models for computer vision systems on robotic platforms, specifically for automotive shopping experiences. It involves end-to-end solution delivery, from design and implementation to optimization and deployment on the edge, with a strong emphasis on deep learning and computer vision techniques.

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.

Senior Process Engineer to lead AI-driven engineering initiatives in the Compliance domain, focusing on designing, building, and operating AI-powered solutions to improve Seller compliance outcomes and operational efficiency. Requires deep hands-on expertise in AI/ML development, building/deploying/scaling production AI systems, designing architectures, building ML models and data pipelines, and driving technical collaboration. Experience with Python, cloud platforms (AWS), and ML operations is essential.

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.

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.

Research Scientist role focused on developing and optimizing Generative AI models for edge devices, involving model compression techniques, custom ML hardware, and theoretical understanding of deep learning and information theory. The role involves co-authoring research papers and collaborating with cross-functional teams.

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.

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.

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.

The role focuses on optimizing ML kernel performance for AWS Neuron SDK on custom ML accelerators (Inferentia and Trainium). It involves designing and implementing high-performance compute kernels, analyzing and optimizing kernel-level performance, implementing compiler optimizations, and collaborating with customers and internal teams to enable and optimize ML models. The work is at the hardware-software boundary, combining deep hardware knowledge with ML expertise.

Applied Scientist role focused on the intersection of AI and program analysis for a deep learning compiler stack, optimizing models for ML accelerators like Trainium. Responsibilities include designing, developing, and deploying analyzers, architecting tooling, and publishing research in compiler technology and deep learning systems software.

Software Development Engineer (MLE) for the CreativeX RAPID team at Amazon, focusing on real-time ad personalization and insights. The role involves tailoring visual ad experiences using technologies like latent diffusion models, LLMs, RL, and computer vision to provide dynamically optimized creatives with low latencies. Responsibilities include investigating new generative AI technologies, prototyping, evaluating feasibility, building data pipelines, and developing platforms for ML model deployment.

Senior Delivery Consultant for AWS Professional Services, focusing on designing, implementing, and managing GenAI and ML solutions for customers. The role involves technical guidance, collaboration with stakeholders, and advising on AI/ML trends and technologies, with a strong emphasis on AWS services.

The Machine Learning Compiler Engineer II on the AWS Neuron team will support the development and scaling of a compiler for AWS Machine Learning accelerators (Inferentia and Trainium chips). This role involves architecting and implementing features for the AWS Neuron Software Development Kit (SDK), which optimizes neural network models for custom AWS hardware. The engineer will work with ML frameworks like PyTorch and TensorFlow, contributing to a toolchain aimed at improving ML performance.

Senior Software Development Engineer role focused on building and scaling GenAI and ML solutions within the Amazon Music Catalog team. The role involves developing, deploying, and optimizing LLMs in production, working with distributed systems and data pipelines, and partnering with Applied Scientists and Product Managers to integrate AI-driven capabilities into catalog systems.

Machine Learning Engineer at Amazon Ads focused on Sponsored Products and Brands, re-imagining advertising with generative AI. The role involves designing, coding, and supporting scalable ML pipelines and online serving systems, optimizing ML model performance and infrastructure, and implementing end-to-end solutions. It requires driving technical direction, building and growing teams, and collaborating on product direction. The team operates on a large product catalog with strict latency constraints and works with research scientists to deliver relevant ads.

This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on architecting scalable, reliable, and secure solutions, supporting the adoption of AWS services, and providing feedback to product teams. The role also involves creating technical content and presenting at events.

This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on providing architectural guidance, supporting the adoption of AWS services, and acting as a technical advisor to help startups build scalable, reliable, and secure solutions.

Software Development Engineer role focused on building and optimizing low-latency, scalable connectivity services for Alexa devices. The role involves integrating AI-driven capabilities and AI-powered features into these services, as well as leveraging AI-augmented development tools to accelerate the software development lifecycle. The team operates at a massive scale, handling billions of transactions daily, and is increasingly adopting an AI-first approach.

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences. The role involves designing and implementing software, creating infrastructure for LLMs in audio, developing tools to evaluate and improve model accuracy, reduce latency, and influence the operational roadmap for core audio services. The position also requires mentoring junior engineers and contributing to hiring efforts.

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences. The role involves designing and implementing software, creating infrastructure for LLMs in audio, developing tools to evaluate and improve model accuracy, reduce latency, and influence the operational roadmap for core audio services. The position also requires mentoring junior engineers and contributing to hiring efforts.

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences, aiming to improve model accuracy and reduce latency. The role involves designing and delivering software, creating infrastructure for LLMs, developing tools for evaluation, and influencing the operational roadmap for core audio services.

Software Development Engineer role focused on enhancing Alexa's audio experiences by integrating Large Language Models (LLMs), improving model accuracy, reducing latency, and building scalable API platforms. The role involves designing, implementing, and delivering software features, creating infrastructure for LLM integration, and influencing the operational excellence roadmap for audio services.

Software Development Engineer role focused on building AI/ML-powered products and infrastructure for data integration workflows within Amazon's AWS Utility Computing organization. The role involves designing, developing, and optimizing AI/ML systems, collaborating with data scientists, and contributing to the full software development lifecycle.

This role is for a Senior Delivery Consultant focused on AI/ML solutions within AWS Professional Services. The consultant will design, implement, and manage AWS-based AI/ML applications and models for customers, providing technical guidance and support throughout the project lifecycle. Key responsibilities include gathering requirements, proposing model training and deployment strategies, and acting as a trusted advisor on AI/ML trends. The role requires experience with AWS AI/ML services and deploying LLMs in production.

This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on architecting scalable, reliable, and secure solutions, supporting the adoption of AWS services, and providing feedback to product teams. The role also involves creating technical content and presenting at events.

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences, aiming to improve model accuracy and reduce latency. The role involves designing and delivering software, creating infrastructure for LLMs, developing tools for evaluation, and influencing the operational roadmap for core audio services.

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences, aiming to improve model accuracy and reduce latency. The role involves designing and delivering software, creating infrastructure for LLMs, developing tools for evaluation, and influencing the operational roadmap for core audio services.

This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on providing architectural guidance, supporting the adoption of AWS services, and acting as a technical advisor to help startups build scalable, reliable, and secure solutions.

Senior Software Engineer role focused on building and optimizing the AWS Neuron compiler for AWS Inferentia and Trainium chips. The role involves transforming ML models (PyTorch, TensorFlow, JAX) into optimized implementations for deep learning workloads, with a focus on large language models and vision transformers. Responsibilities include compiler optimization, working with chip architects, and contributing to open-source communities.

This role focuses on building and operating AWS cloud infrastructure specifically for AI training and inference workloads. The Systems Development Engineer will design, deliver, and operate server solutions that enable high performance and scalability for AI/ML and HPC. The role involves creating automation through agentic workflows and implementing AI-driven tools, impacting both AI implementation and core architecture within the AWS Hardware Engineering team.

Software Development Engineer role focused on integrating Generative AI and LLM capabilities into Alexa's audio experiences. The role involves designing and implementing software, creating infrastructure for LLMs in audio, developing tools to evaluate and improve model accuracy, reducing latency, and influencing operational excellence for audio services. It also includes mentoring junior engineers and contributing to hiring efforts.