Senior Software Development Engineer

Senior Software Development Engineer - AI/ML, AWS Neuron, Multimodal Inference Job ID: | Amazon.com Services LLC The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon’s custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX, enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture while maximizing performance for AWS's custom ML accelerators. Working across the stack from PyTorch to the hardware-software boundary, engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine‑tuned for optimal performance for our customers' demanding workloads. As part of the broader Neuron organization, the team works across multiple technology layers—frameworks, kernels, compiler to runtime and collectives—optimizing current performance and contributing to future architecture designs while collaborating closely with customers to enable their models. This role offers a unique opportunity to work at the intersection of machine learning, high‑performance computing, and distributed architectures, where you’ll help shape the future of AI acceleration technology. Key Responsibilities Architect and implement business‑critical features and mentor a team of experienced engineers. Lead the efforts in building distributed inference support for PyTorch in the Neuron SDK and tune these models for maximum performance on AWS Trainium and Inferentia silicon. Design, develop, and optimize machine learning models and frameworks for deployment on custom ML hardware accelerators. Participate in all stages of the ML system development lifecycle, including distributed computing architecture design, implementation, performance profiling, hardware‑specific optimizations, testing, and production deployment. Build infrastructure to systematically analyze and onboard multiple models with diverse architectures. Design and implement high‑performance kernels and features for ML operations, leveraging the Neuron architecture and programming models. Analyze and optimize system‑level performance across multiple generations of Neuron hardware. Conduct detailed performance analysis using profiling tools to identify and resolve bottlenecks. Implement optimizations such as fusion, sharding, tiling, and scheduling. Conduct comprehensive testing, including unit and end‑to‑end model testing with continuous deployment and releases through pipelines. Work directly with customers to enable and optimize their ML models on AWS accelerators. Collaborate across teams to develop innovative optimization techniques. A Day in the Life You will collaborate with a cross‑functional team of applied scientists, system engineers, and product managers to deliver state‑of‑the‑art inference capabilities for Generative AI applications. Your work will involve debugging performance issues, optimizing memory usage, and shaping the future of Neuron’s inference stack across Amazon and the open source community. You will also build high‑impact solutions for our large customer base, participate in design discussions, code reviews, and communicate with internal and external stakeholders. Working in a startup‑like environment, you’ll focus on the most important initiatives. About the Team The Inference Enablement and Acceleration team fosters a builder’s culture where experimentation is encouraged and impact is measurable. We emphasize collaboration, technical ownership, and continuous learning, supporting new members with mentorship and code reviews. Our senior members enjoy one‑on‑one mentoring and thorough but kind guidance, helping your career growth and empowering you to tackle complex tasks. Basic Qualifications 5+ years of non‑internship professional software development experience. Bachelor's degree or equivalent in Computer Science. 5+ years of non‑internship design or architecture experience of new and existing systems. Fundamentals of Machine Learning and LLMs, their architecture, training, and inference lifecycles, with experience optimizing model execution. Software development experience in C++ and Python (at least one language is required). Strong understanding of system performance, memory management, and parallel computing principles. Proficiency in debugging, profiling, and implementing best software engineering practices in large‑scale systems. Preferred Qualifications Familiarity with PyTorch, JIT compilation, and AOT tracing. Familiarity with CUDA kernels or equivalent ML or low‑level kernels. Experience with performance kernel development such as CUTLASS, FlashInfer, etc. Familiarity with syntax and tile‑level semantics similar to Triton. Experience with online/offline inference serving using vLLM, SGLang, TensorRT, or similar platforms in production environments. Deep understanding of computer architecture, operating system level software, and parallel computing. Equal Opportunity Employer Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $151,300/year in our lowest geographic market up to $261,500/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job‑related knowledge, skills, and experience. Amazon is a total compensation company. Depending on the position offered, equity, sign‑on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. #J-18808-Ljbffr