Principal Machine Learning Engineer, Distributed vLLM Inference

Red Hat • Boston, MA

1d

About The Position

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. As a Principal Machine Learning Engineer focused on distributed vLLM infrastructure in the llm-d project, you will collaborate with our team to tackle the most pressing challenges in scalable inference systems and Kubernetes-native deployments. Your work with distributed systems and cloud infrastructure will directly impact enterprise AI deployments. You would be j oining the core team behind 2025's most popular open source project on GitHub .! If you want to solve challenging technical problems in distributed systems and cloud-native infrastructure the open-source way, this is the role for you. Join us in shaping the future of AI!

Requirements

• 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.
• 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.

Nice To Haves

• Working knowledge of high-performance networking protocols and technologies including UCX, RoCE, InfiniBand, and RDMA is a plus.
• 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 OpenTelemetry.
• Ph.D. in an ML-related domain is a significant advantage

Responsibilities

• 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.

Benefits

• 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!

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 5,001-10,000 employees