The opportunity
OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, our custom silicon program, the team works across silicon architecture, RTL design, verification, physical…
What you'll do
Build developer tools and workflows: Partner with hardware engineering teams to understand their development processes, identify bottlenecks, and build tooling, services, and APIs that automate workflows and accelerate engineering productivity.
Develop and scale regression infrastructure: Design, implement, and operate large-scale regression systems spanning job scheduling, execution, results collection, triage, and reporting, with a focus on performance, reliability, and reproducibility.
Own CI/CD infrastructure: Build and maintain automated pipelines for infrastructure, developer tooling, and cluster configurations, enabling safe deployments, testing, and continuous improvement.
Scale cloud and HPC environments: Design, provision, and operate cloud infrastructure and high-performance compute clusters, optimizing scheduling, capacity, reliability, and cost efficiency to support demanding silicon development workloads.
Build data infrastructure and observability: Develop data pipelines, databases, and monitoring systems that provide visibility into regression results, infrastructure health, resource utilization, and engineering efficiency.
Drive platform reliability and operational excellence: Troubleshoot complex infrastructure issues, improve monitoring and alerting, automate recovery processes, and build systems that are resilient, maintainable, and scalable.
What they're looking for
- Collaborate across hardware and software teams: Work directly with engineers across RTL, verification, physical design, emulation, and software to continuously improve the infrastructure supporting OpenAI's silicon development.
- Have strong software engineering fundamentals, with proficiency in Python or: a similar programming language and experience building reliable automation, tools, or infrastructure services.
- Bring deep technical expertise in at least one area of infrastructure: engineering, such as Linux/HPC environments, cloud infrastructure, CI/CD systems, storage, networking, or EDA development workflows.
- Have experience designing, deploying, and operating cloud infrastructure,: preferably Azure, with familiarity in infrastructure-as-code tools such as Terraform or Bicep.
