Red Hat
AI

Machine Learning Engineer, Distributed vLLM

Red Hat · Boston, MA, US · $136k - $225k

Actively hiring Posted about 2 months ago

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
  • 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

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!

About the company

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. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact.

Tags & focus areas

Used for matching and alerts on DevFound
Fulltime Remote Ai Machine Learning Generative Ai
Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.