Software Engineer, RL Training Infra

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 7, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Software Engineer, RL Training Infra San Francisco, California, United States About the Team The Post-Training Frontiers team creates the frontier agents OpenAI ships to the world. We do the reinforcement learning training for the agentic models we ship in Codex, ChatGPT, and the API (from o1 to 5.5). Our role consists of (1) shepherding all integrations that should go into the final RL run and deciding what can make it in, (2) babysitting and scaling the final run, and (3) building the research and infra for horizontal integrations, such as improving function calling, factuality, multi-agent capabilities, memory, calibrated thinking, etc. About the Role This role focuses on keeping our frontier RL training runs fast, reliable, and unblocked. You will work across engineering and infrastructure problems as they emerge, from scaling and orchestration issues to inference bottlenecks, numerical problems, and hardware failures, as well as supporting large horizontal integrations in the big run, like multi-agent capabilities or memory. This is a role for a strong generalist who quickly learns anything needed for the task, has high attention to detail, debugs deeply, and is motivated by fixing the highest-impact problem in front of the team. In this role, you will: - Keep large-scale RL training runs moving by jumping into the most urgent engineering and infrastructure problems. - Debug issues across training systems, inference, orchestration, scaling, and distributed infrastructure. - Solve hard technical problems at the boundary between research and engineering: scaling experiments, improving training reliability, debugging distributed systems, reducing latency and

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