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OpenAI

Training - Runtime Foundations Engineer

San Francisco (Remote)$266k–$385kfulltimemidAdded 2 days ago

About this role

Build and optimize the distributed runtime system that orchestrates massive-scale machine learning training across thousands of machines. You'll develop high-performance, fault-tolerant software in Rust and Python that enables researchers to run experiments and frontier-scale model training reliably while maintaining system stability and performance.

What you'll do

  • Design and build software to orchestrate ML workloads across large supercomputer clusters
  • Profile and optimize the stack to support computation at frontier scale
  • Improve reliability, observability, and fault tolerance for long-running distributed jobs
  • Debug complex distributed systems issues across large clusters
  • Work across Python and Rust codebase for runtime foundations
  • Respond to evolving ML system needs to support researcher requirements

What they're looking for

  • Distributed systems design and development
  • Rust programming language
  • Asynchronous and concurrent system programming
  • Linux systems administration and debugging
  • Performance analysis and memory profiling
  • High-performance computing optimization
  • Python
  • Systems-level debugging and troubleshooting

Benefits

  • Hybrid work model (3 days in office per week)
  • Based in San Francisco with relocation assistance available
  • Work on frontier-scale AI infrastructure
  • High ownership environment with light process
  • Strong engineering agency and autonomy
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OpenAI

OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.

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Likely interview questions

  • Tell us about a distributed system you've debugged—what was the issue and how did you approach finding the root cause?
  • Describe your experience optimizing system performance at scale. What tools and techniques do you rely on?
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