Principal Machine Learning Engineer, Distributed vLLM Inference

Red Hat

. Develop and maintain distributed inference infrastructure leveraging Kubernetes APIs, operators, and the Gateway Inference Extension API for scalable LLM deployments.

Posted 4/2/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesLead💰 $189,600 - $312,730 per yearWebsite

Tech Stack

Tools & technologies

CloudGoGRPCKubernetesPythonRust

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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, InfiniBand, 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.
Following is considered 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

Benefits

Comp & perks

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!

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonGoLangRustC++KubernetesAPI gatewaysgRPCHTTP/2KV cache-aware routinginference optimization algorithms

Soft Skills

communicationmentoringcollaborationproblem-solvinginnovation

Certifications

Bachelor's degree in computer scienceMaster's degree in computer engineeringPh.D. in ML-related domain