Distributed Training Engineer, Sora

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• 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

Work mode

Hybrid From the posting source checked Jun 20, 2026

In-office days

2 days From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Distributed Training Engineer, Sora San Francisco, California, United States About the Team The Sora team is working on making video a key capability of OpenAI's foundation models. We are a hybrid research and product team that seeks to understand and expand the capabilities of our video models, while ensuring their reliability and safety. We accomplish this both through directly studying and experimenting with the models, as well as deploying them into the real-world to distribute their benefits widely. About the Role As a Distributed Systems/ML engineer, you will work on improving the training throughput for our internal training framework and enable researchers to experiment with new ideas. This requires good engineering (for example designing, implementing, and optimizing state-of-the-art AI models), writing bug-free machine learning code (surprisingly difficult!), and acquiring deep knowledge of the performance of supercomputers. We're looking for people who love optimizing performance, understanding distributed systems, and who cannot stand having bugs in their code. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Collaborate with researchers to enable them to develop systems-efficient video models and architectures - Apply the latest techniques to our internal training framework to achieve impressive hardware efficiency for our training runs - Profile and optimize our training framework You might thrive in this role if you: - Have experience working with multi-modal ML pipelines - Love diving deep

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