Platform ML Engineering Manager, Inference

About the Team

The Platform ML team builds the ML side of our state-of-the-art internal training framework used to train our cutting-edge models.  We work on distributed model execution as well as the interfaces and implementation for model code, training, and inference.

Our priorities are to maximize training throughput (how quickly we can train a new model) and researcher throughput (how quickly we can develop new models) with the goal of accelerating progress towards AGI.  We frequently collaborate with other teams to speed up the development of new capabilities.

About the Role

We are looking for an experienced engineering manager to help lead critical work on our shared internal inference stack and grow the team.  Our inference stack is primarily built by the Applied AI engineering team and we will improve and extend it for research use cases.

In this role, you will:

• Get SOTA throughput for our most important research models.
• Reduce the time it takes to get efficient inference for new model architectures.
• Collaborate closely with Applied AI engineering to maximize the benefits of our shared internal inference stack.
• Hire world-class AI systems engineers in one of the most competitive hiring markets.
• Coordinate the inference needs of OpenAI's research teams.
• Create a diverse, equitable, and inclusive culture that makes all feel welcome while enabling radical candor and the challenging of group think.

You might thrive in this role if you:

• Have 3+ years of experience in engineering management and 7+ years as an IC working with high scale distributed systems and ML systems.
• Have experience with ML systems, particularly high scale distributed training or inference for modern LLMs.
• Have familiarity with the latest AI research and working knowledge of how these systems are efficiently implemented.
• Care deeply about diversity, equity, and inclusion, and have a track record of building inclusive teams.