Key skills
PythonGoKubernetesIstioCiliumEnvoyWASM filtersCNILayer 7 networkingHTTP/2gRPCAPI gatewaysReverse proxiesvLLMSGLangTensorRT-LLMCC++RustUCXRoCEInfiniBandRDMANVIDIA NsightOpenTelemetryGPU performance benchmarkingHigh performance GPU programmingComputer architectureParallel processingDistributed computingGolangAIMachine LearningMLGenAILLMService MeshCommunication
About this role
Red Hat is the world’s leading provider of enterprise open source software solutions, and they are seeking a Machine Learning Engineer focused on distributed vLLM infrastructure. In this role, you will contribute to the design, development, and testing of new features for Red Hat AI Inference, collaborating on scalable inference systems and Kubernetes-native deployments.
Responsibilities:
- Contribute to the design, development, and testing of new features and solutions for Red Hat AI Inference
- Innovate in the inference domain by participating in upstream communities
- Develop and maintain distributed inference infrastructure leveraging Kubernetes APIs, operators, and the Gateway Inference Extension API for scalable LLM deployments
- Develop and maintain system components in Go and/or Rust to integrate with the vLLM project and manage distributed inference workloads
- Develop and maintain 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
- Develop and test various inference optimization algorithms
- Actively participate in technical design discussions
- Contribute to a culture of continuous improvement by sharing recommendations and technical knowledge with team members
- Collaborate with other engineering and cross-functional teams to deliver on engineering deliverables
- Communicate effectively to team members to ensure proper visibility of development efforts
- Be taught, coached, and mentored by senior members of the team
- Provide timely and constructive code reviews
Requirements:
- Strong proficiency in Python and/or GoLang or similar language
- Experience with cloud-native Kubernetes service mesh technologies/stacks such as Istio, Cilium, Envoy (WASM filters), and CNI
- Working understanding of Layer 7 networking, HTTP/2, gRPC, and the fundamentals of API gateways and reverse proxies
- Knowledge of serving runtime technologies for hosting LLMs, such as vLLM, SGLang, TensorRT-LLM, etc
- Excellent written and verbal communication skills, capable of interacting effectively with both technical and non-technical team members
- Ability work independently in a dynamic, fast-paced environment
- Proficiency in C, C++, or Rust
- Experience with the Kubernetes ecosystem, including core concepts, custom APIs, operators, and the Gateway API inference extension for GenAI workloads
- Working knowledge of high-performance networking protocols and technologies including UCX, RoCE, InfiniBand, and RDMA is a plus
- Experience with GPU performance benchmarking and profiling tools like NVIDIA Nsight or distributed tracing libraries/techniques like OpenTelemetry
- Experience in writing high performance code for GPUs and deep knowledge of GPU hardware
- Strong understanding of computer architecture, parallel processing, and distributed computing concepts
- Bachelor's degree in computer science or related field is an advantage, though we prioritize hands-on experience
- Active engagement in the ML research community (publications, conference participation, or open source contributions) is a significant advantage