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Engineering Manager, Artifacts

This critical Engineering Manager role will be responsible for the entire lifecycle and robust infrastructure supporting OpenAI's core ML artifacts—the essential building blocks of our advanced models. You will lead a talented team focused on developing scalable, reliable systems for versioning, storing, and serving massive amounts of model data and computational results. Key responsibilities include architecting governance around these assets, optimizing the storage layer for performance, and ensuring seamless integration across all applied AI projects. The ideal candidate has experience leading high-scale infrastructure teams within an AI context, understands the unique challenges of managing mutable data artifacts in production ML pipelines, and thrives in a fast-paced, mission-driven environment.

OpenAI San Francisco, United States

(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)

Preparing

1200

Question set

300

Duration

Permanent

Focus areas

Engineering ManagementMLOpsArtifact ManagementApplied AI Systems

People Also Ask (FAQs)

What is the last date to apply for Engineering Manager, Artifacts?

The closing date is listed in the official advertisement — apply as early as possible.

How do I apply for Engineering Manager, Artifacts?

Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.

Is there negative marking in the openai test?

The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.

What is the passing marks for this test?

The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.

How can I prepare for the Engineering Manager, Artifacts test?

Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: Engineering Management, MLOps, Artifact Management, Applied AI Systems.

Quick start

Review the job summary, then jump into prep with the same flow used for exam starts.

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