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Anthropic · San Francisco/New York City/Seattle · Hybrid
TPU Performance Engineer
10/25/2026
Description
As a TPU Performance Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimizing kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance. Strong candidates will have a track record of solving large-scale systems problems and low-level optimization.
Qualifications
Have significant experience optimizing ML systems for TPUs, GPUs, or other accelerators
Are results-oriented, with a bias towards flexibility and impact
Pick up slack, even if it goes outside your job description
Enjoy pair programming (we love to pair!)
Want to learn more about machine learning research
Care about the societal impacts of your work
Nice to have
High performance, large-scale ML systems
Designing and implementing kernels for TPUs or other ML accelerators
Understanding accelerators at a deep level, e.g. a background in computer architecture
ML framework internals
Language modeling with transformers
Implement low-latency, high-throughput sampling for large language models
Adapt existing models for low-precision inference
Build quantitative models of system performance
Design and implement custom collective communication algorithms