Senior ML Systems Engineer: Training Infra

Neura Market; San Francisco (CA); 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, to join the robotics team in San Francisco. You will maintain the training framework and surrounding infrastructure, review code quickly, debug failures across ML systems and infrastructure, and unblock researchers when moving ideas to working training jobs.

We value hands‑on coding, quick adaptation to unfamiliar systems, and a low‑ego, pragmatic approach.

Qualifications

• Strong software engineering fundamentals and code review judgment.
• 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 without slowing the team down.
• 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

Software engineering fundamentals

ML systems

Training frameworks

GPUs

Distributed systems

Infrastructure

Job description

OpenAI is seeking a Senior Software Engineer, ML Systems & Training Infrastructure, to join the robotics team in San Francisco. You will maintain the training framework and surrounding infrastructure, review code quickly, debug failures across ML systems and infrastructure, and unblock researchers when moving ideas to working training jobs.

We value hands‑on coding, quick adaptation to unfamiliar systems, and a low‑ego, pragmatic approach.