Senior Machine Learning Engineer, vLLM

Job Summary

At Red Hat we believe the future of AI is open and we are on a mission to bring the power of open-source LLMs and vLLM to every enterprise.

Red Hat AI – Inference team accelerates AI for the enterprise and brings operational simplicity to GenAI deployments. As leading developers, maintainers of the vLLM project, and inventors of state-of-the-art techniques for model quantization and sparsification, our team provides a stable platform for enterprises to build, optimize, and scale LLM deployments.

As a Senior Machine Learning Engineer focused on model optimization algorithms, you will work closely with our product and research teams to develop SOTA deep learning software. You will collaborate with our technical and research teams to develop LLM training and deployment pipelines, implement model compression algorithms, and productize deep learning research. If you are someone who wants to contribute to solving challenging technical problems at the forefront of deep learning in the open source way, this is the role for you.

Join us in shaping the future of AI!

What you will do

• 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 of 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.

What you will bring

• Strong understanding of machine learning and deep learning fundamentals with 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, MS, or PhD in computer science, computer engineering, or a related field.