Software Engineer, ML Systems & Training Architecture

OpenAI OpenAI · AI Frontier · San Francisco, CA · Research

Software Engineer focused on ML Systems & Training Infrastructure for the OpenAI Robotics team, responsible for maintaining and improving the training framework, debugging ML systems, and unblocking researchers and engineers.

What you'd actually do

  1. Review, improve, and clean up code across training frameworks and adjacent infrastructure.
  2. Identify risky or low-quality changes before they land, and raise the code quality bar without slowing the team down.
  3. Debug issues across ML training systems, GPUs, clusters, networking, and related infrastructure.
  4. Help researchers and engineers unblock broken training jobs, flaky workflows, and brittle internal tooling.
  5. Improve the reliability, maintainability, and usability of the robotics team’s training framework.

Skills

Required

  • Software engineering fundamentals
  • Code review judgment
  • ML systems
  • Training frameworks
  • GPUs
  • Distributed systems
  • Infrastructure
  • Debugging complex systems
  • Reading and debugging unfamiliar codebases
  • High-velocity code shipping
  • Pragmatic judgment
  • Low-ego and responsiveness
  • Experience with messy or AI-generated codebases

Nice to have

  • Robotics domain experience

What the JD emphasized

  • ML systems
  • training frameworks
  • GPUs
  • distributed systems
  • infrastructure
  • debug failures across ML systems and infrastructure
  • unblock researchers and engineers
  • writing, reading, reviewing, and fixing code
  • get productive quickly in unfamiliar systems
  • strong practical judgment
  • code review judgment
  • ML systems, training frameworks, GPUs, distributed systems, infrastructure, or similarly complex technical environments
  • Read and debug unfamiliar codebases quickly, and enjoy getting to root cause
  • Ship high-quality code with strong velocity and pragmatic judgment
  • low-ego, responsive, and motivated by helping researchers and engineers move faster
  • reviewing messy, fast-moving, or AI-generated codebases

Other signals

  • ML systems
  • training frameworks
  • GPUs
  • distributed systems
  • infrastructure