Research Engineer, Scaling

Responsibilities:

• Implementing and optimizing distributed training algorithms on accelerators, and surrounding distributed systems to train frontier models.
• The latest generation hardware accelerators and data center networks.
• Collaborating with ML researchers, accelerator performance optimization teams, network teams, cluster management teams, and beyond.
• The high-leverage nexus of systems and machine learning.

You may be a good fit if you:

• Have experience with Python and one additional high performance language (Rust, C, C++, D, Java, C#, Fortran, Go, Swift, etc.)
• Are results-oriented, with a bias towards flexibility and impact
• Enjoy being empowered to fix problems wherever they show up.
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work
• Have clear written and verbal communication

Strong candidates may also have experience with:

• Have experience working with hardware accelerators, HPC, machine learning, networking, or distributed systems
• Have experience or a strong interest in machine learning
• Have experience with complex shared codebases
• Have experience optimizing the performance of programs
• Have experience running highly available systems

Deadline to apply: None. Applications will be reviewed on a rolling basis.