Principal Machine Learning Engineer, Distributed vLLM Inference

Principal Machine Learning Engineer, Distributed vLLM Inference

Join to apply for the Principal Machine Learning Engineer, Distributed vLLM Inference role at Red Hat

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 Inference team accelerates AI for the enterprise and brings operational simplicity to GenAI deployments. As leading developers, maintainers of the vLLM and LLM‑D projects, 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.

Join us in shaping the future of AI!

• Develop and maintain distributed inference infrastructure leveraging Kubernetes APIs, operators, and the Gateway Inference Extension API for scalable LLM deployments.
• Create system components in Go and/or Rust to integrate with the vLLM project and manage distributed inference workloads.
• Design and implement KV cache‑aware routing and scoring algorithms to optimize memory utilization and request distribution in large‑scale inference deployments.
• Enhance the resource utilization, fault tolerance, and stability of the inference stack.
• Contribute to the design, development, and testing of various inference optimization algorithms.
• Actively participate in technical design discussions and propose innovative solutions to complex challenges.
• Provide timely and constructive code reviews.
• Mentor and guide fellow engineers, fostering a culture of continuous learning and innovation.

• Strong proficiency in Python, GoLang and at least one of the following:
  Rust, or C++.
• Experience with cloud‑native Kubernetes service mesh technologies/stacks such as Istio, Cilium, Envoy (WASM filters), and CNI.
• A solid understanding of Layer 7 networking, HTTP/2, gRPC, and the fundamentals of API gateways and reverse proxies.
• Working knowledge of high‑performance networking protocols and technologies including UCX, RoCE, Infini Band, and RDMA is a plus.
• Excellent communication skills, capable of interacting effectively with both technical and non‑technical team members.
• A Bachelor's or Master's degree in computer science, computer engineering, or a related field.

• Experience with the Kubernetes ecosystem, including core concepts, custom APIs, operators, and the Gateway API inference extension for GenAI workloads.
• Experience with GPU performance benchmarking and profiling tools like NVIDIA Nsight or distributed tracing libraries/techniques like Open Telemetry.
• Ph.D. in an ML‑related domain is a significant advantage.

The salary range for this position is $ – $. Actual offer will be based on your qualifications.

• Comprehensive medical, dental, and vision coverage
• Flexible Spending Account – healthcare and dependent care
• Health Savings Account – high deductible medical plan
• Retirement 401(k) with employer match
• Paid time off and holidays
• Paid parental leave plans for all new parents
• Leave benefits including disability, paid family medical leave, and paid military leave
• Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!

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