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AI Systems Engineer, Architecture (Training)

OpenAI
On-site
San Francisco, CA, USA
Full-time
$360,000 - $430,000

About the Role

As a senior engineer for the Architecture Systems Team, you will be an expert on the frameworks used by OpenAI for large-scale training, efficient sampling, architecture and optimization research, and simulating model performance.

You will collaborate closely with both researchers and engineers across the company to integrate new advances into our training and sampling stacks and make foundational improvements to the underlying frameworks. You will own the benchmarking and simulation tools that OpenAI uses to accurately estimate the performance of and design the configuration of new models. You will help set direction for the architecture systems team, mentor more junior members of the team, and help guide the evolution of our ML frameworks.

We’re looking for people who love understanding things at a very deep level, care about both well-designed APIs and systems efficiency, are excited about working at the boundary of research and engineering and collaborating across teams, and who are able to reason across all layers of our stack, from the ML algorithm all the way down to the hardware. Your work will directly impact the capability of our flagship models.

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:

Become an expert in the frameworks used by OpenAI for large-scale training, efficient sampling, architecture and optimization research, and simulating model performance

Work with researchers to ship new advances in OpenAI's flagship models

Work with the platform teams to make foundational improvements to our ML frameworks



You might thrive in this role if you:

Love collaborating across teams and working at the boundary of engineering and research

Care about both well-designed APIs and systems efficiency

Have experience with the systems and frameworks used in LLM training and deployment

Love understanding and debugging systems across all layers of abstraction

Have strong software engineering skills and are proficient in Python
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