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Member of Engineering (Reinforcement Learning Infrastructure)

Skills
Software DevelopmentConcurrent ProgrammingDistributed SystemsEngineeringProgrammingPythonReinforcement Learning
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • Experience with LLMs and model post-training workflows
  • Understanding of Reinforcement Learning and its main bottlenecks
  • Solid software engineering fundamentals including testing code review and debugging complex systems
  • Proficiency in Python with concurrency asynchronous programming multiprocessing and performance optimization
  • Familiarity with PyTorch or JAX and RL workflows such as rollouts replay buffers and policy updates
  • Experience designing and maintaining distributed RL training systems
  • Experience with large-scale LLM training infrastructure
  • Experience with profiling tools across the stack such as py-spy
  • Experience with inference stacks such as vLLM

Nice to have

  • Open-source contributions to RL projects
  • Open-source contributions to distributed ML projects

Day to day

  • Build and scale infrastructure for reliable and efficient frontier RL training of Large Language Models
  • Research and develop new exploration and training algorithms while designing and scaling RL environments
  • Implement end-to-end solutions across the stack and optimize performance across networking memory compute scheduling and I/O

Hiring process

  • Intro call with one of our Founding Engineers
  • Technical Interview(s) with one of our Founding Engineers
  • Team fit call with the People team
  • Final interview with one of our Founding Engineers