About the role

Senior Software Engineer building and releasing the Red Hat AI Inference Server. Working across the full stack from C++/CUDA kernel compilation to Kubernetes-orchestrated model serving.

Responsibilities

Build and release vLLM wheels across multiple hardware backends and CPU architectures, managing complex native dependency chains including PyTorch, Triton, and other accelerator-specific libraries
Design and maintain CI/CD pipelines spanning multiple platforms including GitHub Actions, GitLab CI, and Buildkite for build, test, and release workflows
Manage and scale multi-cloud GPU infrastructure using Terraform and Ansible, including both bare-metal and Kubernetes-based compute runners
Own the model validation pipeline, orchestrating accuracy evaluation, performance benchmarking, tool-calling validation, and smoke testing across dozens of LLMs on both bare metal and OpenShift
Develop and maintain the Python tooling and automation that powers the build, packaging, validation, and release processes
Drive adoption of agentic AI and intelligent automation to streamline engineering workflows, accelerate debugging, and reduce toil across the team

5+ years of software engineering experience with significant depth in build systems, release engineering, or infrastructure
Strong Python development skills with experience building well-tested, maintainable tooling and automation
Hands-on experience building and packaging Python projects with native compiled extensions, including familiarity with C++ and CUDA build toolchains, wheel packaging, and multi-architecture builds
Deep familiarity with container ecosystems, including Dockerfiles and Containerfiles, image registries, and container build pipelines
Understanding of LLM evaluation methodology, including accuracy benchmarks such as MMLU, GSM8K, and HellaSwag, as well as inference performance metrics like throughput and latency
Experience with CI/CD platforms such as GitHub Actions, GitLab CI, Tekton, or Buildkite
Solid understanding of release engineering practices including reproducible builds, artifact management, dependency pinning, and security scanning
Experience with infrastructure-as-code tools such as Terraform and Ansible, and managing cloud resources at scale
Working knowledge of Kubernetes and/or OpenShift for deploying and testing workloads
Enthusiasm for applying LLM-based agents and AI-assisted tools to automate engineering workflows, with a track record of identifying repetitive processes and replacing them with intelligent automation
Excellent communication skills, capable of interacting effectively with both technical and non-technical team members.

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, and employee assistance program