5-7 years of experience in optimizing machine learning workloads on TPUs
Strong understanding of modern model architectures and their interaction with hardware
Proficient in software engineering with a focus on profiling and debugging
Ability to connect kernel behavior to overall model performance
Experience with quantization or attention kernels is a plus
Demonstrated ability to tackle open-ended technical challenges
Strong collaboration skills with researchers and engineers
Responsibilities
Optimize model execution on TPUs for improved performance
Profile workloads to identify and address performance bottlenecks
Develop and optimize kernels based on model understanding
Explore quantization techniques to enhance inference efficiency
Build benchmarks and tools for performance measurement and validation
Collaborate with teams to support new model architectures
Implement performance improvements into production environments
Benefits
Opportunity to work with cutting-edge AI technology
Broad technical ownership of projects
Collaborative environment with researchers and engineers
Focus on impactful performance improvements
Engagement with innovative model architectures
Full Job Description
About the Role
We're looking for an engineer to lead the optimization of OpenAI's state-of-the-art frontier models on TPUs. You will own performance across the full forward pass, from model code through kernels to execution on the hardware.
You'll have broad technical ownership: identifying performance bottlenecks, choosing the most impactful improvements, and carrying them through implementation and validation. We welcome experience gained through either training or inference, with a strong foundation in optimizing real workloads on TPUs.
In this role, you will:
Optimize model execution on TPUs, improving performance across the complete forward pass and the interactions between model code, kernels, and hardware.
Profile workloads, identify bottlenecks, and implement improvements to latency, throughput, and hardware utilization.
Develop and optimize kernels, using an understanding of the surrounding model to guide implementation choices.
Explore techniques such as quantization to improve inference efficiency while preserving model quality.
Build benchmarks and tools to measure performance, validate correctness, and guide optimization work.
Partner with researchers and engineers to support new model architectures and bring performance improvements into production.
You might thrive in this role if you:
Have practical experience optimizing machine learning workloads on TPUs, through training, inference, or both.
Understand modern model architectures and how their computation maps onto accelerator hardware.
Can connect detailed kernel behavior to the performance of a complete model and work across these layers of the stack.
Bring strong software engineering skills and a systematic approach to profiling, debugging, and validating performance improvements.
Take ownership of open-ended technical problems and can identify priorities, make design decisions, and deliver working solutions.
Enjoys collaborating with researchers and engineers, learning unfamiliar parts of a system, and communicating technical tradeoffs.
Have experience with quantization or attention kernels, which would be especially useful in this role.
About OpenAI
OpenAI is an artificial intelligence research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. The company was founded in 2015 by a group of technology leaders, including Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever, and John Schulman. OpenAI's mission is to develop and promote friendly AI for the betterment of humanity. The company has developed a number of cutting-edge AI technologies, including GPT-3, a language processing system that can generate human-like text. OpenAI has received funding from a number of high-profile investors, including LinkedIn co-founder Reid Hoffman and venture capitalist Peter Thiel.
