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# Performance Engineer

- **Company:** Anthropic Limited
- **Location:** San Francisco, CA, United States
- **Salary:** $280,000.0
- **Contract:** Permanent contract
- **Skills:** Complex Networks, Software Debugging, Distributed Systems, Fault Tolerance, Machine Learning, Language Modeling, Pair Programming, Software Engineering, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_c8308d76f543c8d366e73e90cd80ffaf

## About the Role

* 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, Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

## Description

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., * 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