AWS Neuron is the software stack for AWS Inferentia and Trainium accelerators for cloud-scale ML. As a senior engineer on the Machine Learning Inference Applications team, you will lead development and optimization of open-source inference frameworks like vLLM and SGLang to deliver high-performance LLM serving on AWS Neuron. You will collaborate with model, compiler, runtime, and performance engineers to ensure end-to-end performance, scalability, and production readiness.
Responsibilities
Lead customization and optimization of open-source inference frameworks (vLLM, SGLang) on AWS Neuron, including core framework logic like scheduling and model execution
Apply state-of-the-art techniques in kernel development, parallel computation, distributed KV cache, and speculative decoding to deliver best-in-class LLM serving performance
Influence the technical roadmap by evaluating emerging inference research and translating it into production-ready features
Provide design leadership and mentorship while collaborating across model development, compiler, runtime, and performance engineering teams
Requirements
5+ years of full software development life cycle experience, including coding standards, code reviews, source control management, build processes, testing, and operations
Bachelor's degree in computer science or equivalent
5+ years of programming in a modern language such as Java, C++, or C#, including object-oriented design
Annapurna Labs is a fabless semiconductor subsidiary of Amazon Web Services (AWS) that designs custom silicon and software infrastructure for cloud computing. Its innovations include the AWS Nitro System, Graviton processors, and machine learning accelerators like Trainium and Inferentia.
Fundamentals of machine learning models, architecture, training and inference lifecycles, with experience in optimizations for improving model performance
Nice to Have
Master's degree in Computer Science or related field
Hands-on experience with PyTorch or JAX, preferably with LLMs in production on GPUs, AWS Neuron, TPUs, or other AI accelerators
Experience extending or contributing to vLLM or SGLang