Senior ML Systems Engineer - Training Infra

OpenAI; California (MO); On-site; USD 295,000 - 380,000; Full time

Benefits offered by this job

Relocation assistance

Job summary

OpenAI is seeking a Senior Software Engineer, ML Systems & Training Infrastructure, based in San Francisco, CA, with relocation assistance offered. You will review, improve, and debug training frameworks, support researchers and engineers, and move fast solving complex ML infrastructure problems.

Strong coding and practical judgment required. Join a team focused on robotics and AI systems across the model stack to enable scalable, reliable training workflows.

Qualifications

• Experience with 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.

Responsibilities

• Review, improve, and clean up code across training frameworks and adjacent infrastructure.
• Identify risky or low‑quality changes before they land and raise the code quality bar.
• Debug issues across ML training systems, GPUs, clusters, networking, and related infrastructure.
• Help researchers and engineers unblock broken training jobs, flaky workflows, and brittle internal tooling.
• Improve the reliability, maintainability, and usability of the robotics team’s training framework.
• Move quickly on practical engineering problems that directly affect team velocity.

Skills

ML systems

Training infra

GPU clusters

Distributed systems

Code review

Debugging

Job description

OpenAI is seeking a Senior Software Engineer, ML Systems & Training Infrastructure, based in San Francisco, CA, with relocation assistance offered. You will review, improve, and debug training frameworks, support researchers and engineers, and move fast solving complex ML infrastructure problems.

Strong coding and practical judgment required. Join a team focused on robotics and AI systems across the model stack to enable scalable, reliable training workflows.