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Senior Principal Software Engineer, AI Inference
Red Hat. 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 .
Posted 4/8/2026full-timeBoston • Massachusetts, North Carolina • 🇺🇸 United StatesSenior💰 $189,600 - $312,730 per yearWebsite
Tech Stack
Tools & technologiesAnsibleCloudKubernetesOpenShiftPythonPyTorchTerraform
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- 8+ 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.
- A Bachelor's or Master's degree in computer science, computer engineering, or a related field. A 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 resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonC++CUDACI/CDbuild systemsrelease engineeringinfrastructure-as-codecontainer ecosystemsLLM evaluation methodologyartifact management
Soft Skills
communicationteam collaborationproblem-solvingautomationdebuggingprocess improvementtechnical writinginterpersonal skillsleadershipadaptability
Certifications
Bachelor's degree in computer scienceMaster's degree in computer engineeringPh.D. in ML-related domain