Performance Engineer

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.