Contribute to the design, development, and testing of various inference optimization algorithms in the vLLM and related projects, such as llm-d
Create and manage inference serving deployment pipelines
Benchmark, profile, and evaluate different parallelizations, quantization, and sparsification approaches to determine the best performance for specific hardware and models
Stay up-to-date with the latest advancements in the open source LLM model architecture, LLM Inference parallelizations/optimizations techniques, and quantization research
Stay up-to-date with the latest CPU and GPU hardware architecture and features to boost AI inference performance
Give thoughtful and prompt code reviews
Continuous collaboration with internal and external open source committers and contributors while contributing to vLLM and related projects

Strong understanding of machine learning and deep learning fundamentals
Experience in one or more of LLM Inference Optimizations, Computer Vision, NLP, and reinforcement learning
Experience with tensor math libraries such as PyTorch and NumPy
Strong programming skills with proven experience implementing Python based machine learning solutions
Ability to develop and implement research ideas and algorithms
Experience with mathematical software, especially linear algebra
Understanding of Linear Algebra, Gradients, Probability, and Graph Theory
BS, or MS, or PhD in computer science or computer engineering or a related field.

Senior Machine Learning Engineer, vLLM

Key skills