Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $177K – $327K, with generous equity, performance-related bonuses for eligible employees, and the following benefits.
Benefits include:
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends
About the Team
OpenAI's Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. The team is working on future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models.
About the Role
As a Software Engineer, DevOps, you will define and build the CI/CD systems that qualify frontier models and the software stack running on OpenAI's custom AI accelerators. Your work will make correctness, performance, and stability measurable on every change, from individual commits through production releases.
Responsibilities include:
- Defining the model CI strategy for frontier models running on OpenAI custom silicon and integrated accelerator systems
- Building production CI/CD pipelines for functional regression, performance benchmarking, software stability, and release qualification
- Designing representative test matrices across models, configurations, hardware generations, software components, and deployment environments
- Creating reliable performance baselines, regression detection, bisect and triage workflows, and clear ownership for failures
- Establishing GitOps practices for reproducible configuration, promotion, rollback, auditability, and environment consistency
- Shaping monorepo architecture, dependency management, build and test boundaries, change validation, and developer workflows at scale
- Developing scalable orchestration, artifact management, caching, scheduling, observability, and capacity controls for accelerator-backed CI
- Partnering with model, compiler, kernel, runtime, firmware, validation, and hardware teams to translate release risks into automated gates
- Improving CI reliability, speed, debuggability, and cost efficiency while maintaining high confidence in production software
Requirements
You might thrive in this role if:
- You have built or operated large-scale CI/CD, developer infrastructure, test automation, or production engineering systems
- You understand modern software delivery practices, including GitOps, release promotion, rollback, reproducibility, and policy-driven automation
- You have experience designing monorepos, build systems, dependency graphs, test selection, or change-impact analysis
- You can design regression and performance-benchmarking systems with stable baselines, useful metrics, and actionable failure diagnosis
- You are proficient in Python, Go, Rust, C++, or another language used to build reliable infrastructure and automation
- You have worked with distributed systems, schedulers, containers, clusters, or heterogeneous compute infrastructure
- You can collaborate with model and systems engineers to turn complex accelerator workloads into repeatable production qualification
- You care deeply about reliability, observability, developer experience, and reducing time from code change to trustworthy signal