Senior Software Development Engineer role focused on developing advanced robotics systems at Amazon scale, integrating cutting-edge AI, control systems, and mechanical design for adaptable automation solutions. The role involves leading a team to design and implement a software framework for generalized dexterous mobile manipulation, including cloud and on-edge inferencing, real-time teleoperation, and human-machine interfaces.

Internship role focused on building AI infrastructure and ML systems, including custom silicon design, framework optimization, compiler development, and distributed training systems for AWS.

Software Development Engineer II role focused on building and deploying machine learning models and systems for profit intelligence within Amazon. Responsibilities include production deployment, architecture design, automation of ML pipelines, and metrics design for model effectiveness. The role leverages AWS cloud services and distributed systems.

Software Development Engineer focused on real-time ad personalization and insights, leveraging generative AI technologies like latent diffusion models, LLMs, and RL to tailor visual ad experiences. The role involves investigating new technologies, building data pipelines, and developing platforms for ML model deployment in the Dynamic Creative Optimization (DCO) domain.

Software Development Engineer on the EC2 Nitro Machine Learning Systems team, focusing on building and optimizing performance measurement infrastructure for AI/ML workloads. The role involves establishing best-known configurations, translating performance insights into technical requirements, and analyzing training/inference performance across accelerated platforms. It requires expertise in low-level systems, ML frameworks, and serving layers.

Senior Software Development Engineer role focused on building and scaling EC2 compute platforms for machine learning workloads, including training and inference for LLMs and multimodal systems. The role involves designing innovative technologies, leading technical projects, developing regression testing systems, and collaborating with hardware teams to optimize platform designs for ML performance.

Senior Software Development Engineer role focused on building and scaling machine learning infrastructure for EC2 Nitro, supporting training and inference workloads for various ML applications including LLMs and multimodal systems. The role involves designing innovative technologies, leading technical projects, developing regression testing systems, and collaborating with hardware teams to optimize platform designs for ML performance.

Applied Scientist II role focused on enhancing ML accelerator software (Trainium/Inferentia) to accelerate customer adoption. Responsibilities include developing ML/RL for code generation/optimization, creating ML compiler techniques, building validation tools, and designing high-performance kernels. The role involves working with customers, engineering teams, and research communities to advance ML systems, with a focus on inference performance and training cost optimization.

This role focuses on integrating computer vision, edge computing, and physical automation systems to enable real-time operational intelligence, improve equipment performance, and optimize process flow within global fulfillment networks. The engineer will bridge AI/ML models with physical systems, leading the development and deployment of sensor-driven automation solutions and ensuring seamless integration across hardware, software, and control layers.

Senior Applied Scientist role focused on building predictive intelligence for capacity management in AWS workspaces. This involves developing ML systems for demand forecasting, resource optimization, and cost efficiency at enterprise scale. The role requires translating business needs into production ML systems, designing algorithms, and applying advanced ML techniques like time-series forecasting, reinforcement learning, and causal inference. Emphasis on low-latency, large-scale data processing, and collaboration with product and engineering teams.

ML Compiler Engineer II on the Neuron Compiler Automated Reasoning Group, developing and maintaining tooling for fuzzers and specification synthesis for an LLVM-based compiler targeting ML accelerators (Inferentia/Trainium) for domains like Large Language and Vision. Focus on accuracy and reliability of the compiler stack.

Software Development Engineer II on the AWS Elemental Inference team, focused on building and shipping production code for AI-driven video processing systems. The role involves translating research into scalable services, improving performance, and implementing MLOps best practices for AI-enhanced systems.

Applied Scientist role focused on using machine learning and statistical techniques to create and deploy advanced algorithmic systems for optimizing business operations and customer value within Amazon's consumer businesses. The role involves end-to-end ownership of business problems, from data analysis and model development to real-time implementation and scaling.

Software Development Engineer role focused on building and scaling foundational services for computer vision and AI capabilities within Amazon Photos. The role involves architecting and developing solutions for processing large volumes of photo and video data, optimizing ML model serving infrastructure, and collaborating with ML scientists to productionize models.

Applied Scientist role focused on building and deploying machine learning models for Amazon's Product Search, with an emphasis on developing new ranking features, ensuring low-latency predictions, and scaling systems to handle billions of search requests. The role involves full development cycle ownership, from design and implementation to A/B testing and production deployment, leveraging academic research and contributing to the search experience in new and existing markets.

Software Development Engineer role focused on building and operating the data platform infrastructure for an AI-driven analytics platform at AWS Identity. This involves designing and managing ingestion, transformation, and serving pipelines for petabyte-scale data to feed ML models and LLM agents. The role also includes productionizing ML models, building feature engineering infrastructure, and ensuring platform resilience and scalability.

This role focuses on the operational excellence and reliability of a fleet of ML accelerators and server products, specifically Amazon's Trainium chips. The engineer will be responsible for debugging hardware and software issues, developing automation, analyzing fleet data, and ensuring the health and performance of the ML hardware infrastructure. This is an engineering role focused on the operational aspects of serving ML hardware.

Software Development Engineer role focused on building AI/ML-powered products and infrastructure for data integration workflows at Amazon.

Senior Software Development Engineer to build and optimize infrastructure for AI/ML workloads on EC2 Nitro. Focus on performance measurement, benchmarking, regression testing, and influencing future hardware designs for LLMs, multimodal systems, and emerging architectures. Role involves both customer-facing performance problem-solving and foundational infrastructure development.

Software Development Engineer to build and optimize performance measurement infrastructure for AI/ML workloads on AWS EC2 Nitro. The role involves low-level systems, ML frameworks, and serving layers to translate performance insights into technical requirements for platform designs.

Software Development Engineer to build and optimize performance measurement infrastructure for AI/ML workloads on AWS EC2 Nitro. The role involves low-level systems, ML frameworks, and serving layers to translate performance insights into technical requirements for platform designs.

Software Development Engineer II, Post Silicon Validation for AWS's next-generation machine learning accelerators. This role involves validating the complete vertical stack of ML accelerators, from silicon to system, ensuring quality and performance for AWS cloud infrastructure. Responsibilities include developing validation strategies, executing test plans, hardware bring-up and debug, and collaborating with cross-functional teams.

Senior Software Development Engineer focused on developing and deploying edge AI solutions with a specialization in computer vision for consumer devices. The role involves designing and implementing efficient edge AI architectures, collaborating with applied science and cloud teams, and optimizing system performance.

This role focuses on building and operating AWS cloud infrastructure for AI training and inference, specifically targeting high-performance and scalable solutions for large language models. The engineer will work on server designs, system-level debugging, and implementing automation solutions, including agentic workflows and AI-driven tools, to enhance the productivity of other engineers and influence AI implementation and core architecture.

Software Development Engineer role within AWS Applied AI Solutions, focusing on building and scaling the Machine Learning platform for Just Walk Out (JWO) Technology. The role involves solving problems in computer vision, image recognition, machine learning, and distributed systems, with a need for entrepreneurial spirit and full-stack development skills.

Software Development Engineer role within AWS Applied AI Solutions focused on building and scaling the Machine Learning platform for Just Walk Out (JWO) Technology. This involves working with computer vision, image recognition, machine learning, and distributed systems to power checkout-free shopping experiences. The role requires strong software engineering skills, including full-stack development, and experience with Java and AWS services.

Software Development Engineer role focused on building and scaling AI-powered computer vision cloud services for Ring consumer electronics, serving tens of millions of users globally. Responsibilities include product definition, design, development, deployment, and operations of highly available and resilient cloud services.

Machine Learning Engineer II on an Amazon Special Projects team focused on creating new products and services using Generative AI and LLMs. Responsibilities include developing and maintaining platforms for LLM development, evaluation, and deployment, processing large datasets, scaling models, and optimizing performance. Experience with distributed model training is required.

This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models at scale. The individual will act as a trusted advisor, architecting solutions, supporting adoption of AWS services, and providing feedback to product teams. The role also involves creating technical content and presenting at events.

The Sr. Software Development Manager will lead a team of compiler engineers developing, deploying, and scaling a compiler targeting AWS Inferentia and Trainium ML accelerators. This role involves deep knowledge of resource management, scheduling, code generation, and optimization for new instruction architectures, with a focus on delivering high-performance, low-cost ML inference and training solutions for AWS customers.

Software Engineer focused on performance optimization for distributed training of large-scale AI/ML models (LLMs, multi-modal) on AWS Neuron accelerators. This involves tuning across the software stack, including collective communications, memory utilization, compiler optimizations, and kernel performance, working with PyTorch and JAX.

Seeking a Software Engineering Manager to lead a team developing compiler optimization algorithms and deploying a new compiler for AWS custom hardware (Inferentia and Trainium chips). The role involves technical leadership, mentoring, and partnering with AWS ML services teams to improve deep learning model performance and productivity.

The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads.

Software Development Engineer II at Amazon's Annapurna Labs, focusing on distributed AI/ML systems and collective operations for scaling AI across multiple accelerators and servers. The role requires strong C/C++ and Linux skills, with experience in embedded systems, high-speed networking, or HPC interconnects being valuable. This position is on the forefront of AI/ML, working with large-scale clusters and models within AWS's EC2 infrastructure.

This role is for a Sr. Machine Learning Compiler Engineer III on the AWS Neuron team, focusing on the development and scaling of a compiler for ML accelerators. The role involves architecting and implementing features for a deep learning compiler stack that optimizes neural network performance on custom AWS hardware, integrating with frameworks like PyTorch and TensorFlow. The goal is to provide significant performance improvements for large-scale ML workloads.

This role focuses on building and deploying reliable, scalable, and high-performance ML/AI solutions, leveraging Big Data, AppDev, or DevOps experience. It involves working closely with Data Scientists and Data Engineers to deliver end-to-end solutions, utilizing ML frameworks, algorithms, and ML pipelines, with a strong emphasis on hosting and deployment of models.

Software Development Manager for AWS Neuron Tools team, responsible for leading engineers to develop and maintain high-performance monitoring and profiling tools for AI accelerators (Inferentia, Trainium). The role involves managing the full development lifecycle of the Neuron Profiler, ensuring scalability, reliability, and usability, and collaborating with cross-functional teams to optimize AI workloads. Experience with ML-specific profiler tools and performance analysis is required.

Manager, Software Development for Alexa AI in India, focusing on speech and language solutions. The role involves leading a team to pioneer ML tools and processes, build scalable applications, and contribute to system architecture and technical vision for Alexa AI.

Software Development Engineer focused on building and applying AI agents to simplify and accelerate customer adoption of AWS Neuron ML chips (Trainium and Inferentia). The role involves working with external and internal customers to identify obstacles and opportunities for accelerating adoption, and transforming service performance, durability, cost, and security.

Software Development Engineer focused on building and applying AI agents to simplify and accelerate customer adoption of AWS Neuron ML chips (Trainium and Inferentia). The role involves working with external and internal customers to identify obstacles and opportunities for accelerating adoption, and transforming service performance, durability, cost, and security.

The Applied Scientist Intern will work on improving Amazon's product search service by designing and integrating machine learning models using TB-scale data. The role involves balancing business metrics and response times, and requires a PhD student in Computer Science, AI, ML, or related fields with experience in ML experiment design, statistical analysis, and coding.

Machine Learning Engineer role focused on optimizing and tuning inference performance for AWS Neuron accelerators, specifically for large language models (LLMs) and other key ML model families. The role involves developing and performance tuning building blocks for the distributed inference library, ensuring high performance and efficiency on Trn2 and Trn3 servers. Requires experience with LLM inference optimization, kernels, Python, PyTorch, or JAX.

This role is for a Senior SoC Power Engineer focused on developing and optimizing power consumption for machine learning accelerators (Inferentia and Trainium SoCs) within AWS. The engineer will be responsible for power analysis and modeling from RTL to netlist, identifying power saving opportunities, and correlating simulation results with lab measurements. This is an engineering role focused on the hardware infrastructure that powers AI workloads.

Software Engineering Manager to lead a team focused on machine learning compiler design and development for AWS Neuron, driving optimization techniques, hardware bring-up, and influencing pre-silicon design decisions to accelerate ML infrastructure.

This role focuses on optimizing the performance of the AWS Neuron software stack, which supports Generative AI and ML workloads on AWS's custom ML accelerators (Inferentia and Trainium). The engineer will analyze ML workloads, develop high-performance kernels, enhance the Neuron SDK, and collaborate with compiler, frameworks, and hardware teams to maximize end-to-end performance. Responsibilities include instruction scheduling, memory management, parallelism, kernel optimization, and compiler enhancements, with a focus on ML inference and training performance.

The Machine Learning Compiler Engineer will work on the Amazon Neuron team to develop and scale a deep learning compiler stack for Amazon's custom ML accelerators (Inferentia and Trainium). This role involves optimizing neural network models for inference and training performance, integrating with ML frameworks, and contributing to the software stack that enables large-scale ML workloads. The engineer will be involved in pre-silicon design and bringing new features to market.

This role involves developing bare metal firmware for custom ASIC-based ML Accelerator chips, focusing on hardware/software co-design for machine learning acceleration systems. The engineer will work on the firmware that drives neural network model execution on custom silicon, collaborating with hardware design teams. While no prior ML knowledge is required, the role is core to enabling ML infrastructure.

This role focuses on validating next-generation machine learning accelerators for AWS, covering the entire vertical stack from silicon to system. The engineer will develop and execute validation strategies, conduct hands-on bring-up and debug, and collaborate with various teams to ensure the quality and performance of AI/ML accelerators used in AWS data centers for AI training and inference.