Performance Engineer
Anthropic · San Francisco, CA | New York City, NY | Seattle, WA
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: Running machine learning (ML) algorithms at our scale often requires solving novel systems problems. As a Performance Engineer, you'll be responsible for identifying these problems, and then developing systems that optimize the throughput and robustness of our largest distributed systems. Strong candidates here will have a track record of solving large-scale systems problems and will be excited to grow to become an expert in ML also. You may be a good fit if you: Have significant software engineering or machine learning experience, particularly at supercomputing scale 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 Strong candidates may also have experience with: High performance, large-scale ML systems GPU/Accelerator programming ML framework internals OS internals Language modeling with transformers Representative projects: Implement low-latency high-throughput sampling for large language models Implement GPU kernels to adapt our models to low-precision inference Write a custom load-balancing algorithm to optimize serving efficiency Build quantitative models of system performance Design and implement a fault-tolerant distributed system running with a complex network topology Debug kernel-level network latency spikes in a containerized environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. Fo