TPU Kernel Engineer
Anthropic · San Francisco, CA | New York City, NY | Seattle, WA · AI Research & Engineering
About this role
Anthropic is hiring a mid-level Embedded Software Engineer in the software engineering function based in San Francisco, CA | New York City, NY | Seattle, WA. The posting calls out experience with LLMs, Data Structures, Machine Learning, OpenAI. Compensation is listed at $280,000–$850,000 per year.
- Role
- Embedded Software Engineer
- Function
- software engineering
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Francisco, CA | New York City, NY | Seattle, WA
- Department
- AI Research & Engineering
More roles at Anthropic
Job description
from Anthropic careersAbout 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
As a TPU Kernel 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.
You may be a good fit if you:
- 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
Strong candidates may also have experience with:
- High performance, large-scale ML systems
- Designing and implementing kernels for TPUs or other ML accelerators