Principal Machine Learning Engineer, Distributed vLLM Inference and Kubernetes
Red HatAbout the role
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 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 Machine Learning Engineer focused on distributed vLLM infrastructure, 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. 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!
What you will do
Build and maintain distributed inference infrastructure using Kubernetes APIs, operators, and the Gateway Inference Extension API for scalable LLM deployments
Develop systems 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 across large scale inference deployments
Improve the resource utilization, fault tolerance, and stability of the inference stack
Contribute to the design, development, and testing of various inference optimization algorithms
Participate in technical design discussions and provide innovative solutions to complex problems
Give thoughtful and prompt code reviews
Mentor and guide other engineers and foster a culture of continuous learning and innovation
What you will bring
Strong proficiency in Python and one or more system programming languages (Golang, Rust, C++)
Strong understanding of computer architecture, parallel processing, and distributed computing concepts
Experience with the Kubernetes ecosystem, including custom APIs, operators, and the Gateway API inference extension for GenAI workloads (nice to have)
Experience with cloud native Kubernetes service mesh technologies/stacks like Istio, Cillium, Envoy (WASM filters) and CNI
Experience with tensor math libraries such as PyTorch
Working understanding of high-performance networking protocols and technologies including UCX, RoCE, InfiniBand, and RDMA
Mathematical software, especially linear algebra or signal processing
Deep understanding and experience in GPU performance optimizations
Experience optimizing kernels for deep neural networks
Experience with profiling tools like NVIDIA Nsight or distributed tracing libraries/techniques like OpenTelemetry is a plus
Strong communications skills with both technical and non-technical team members
BS, or MS in computer science or computer engineering or a related field. A PhD in a ML related domain is considered a plus
Pay Transparency
Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience.
About Red Hat
Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. S
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