Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Algorithms
Distributed Systems
GPU @ 3
LLM
Machine Learning @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Anthropic is seeking a Performance Engineer to identify novel systems problems involved in running machine learning algorithms at scale and develop systems that optimize the throughput and robustness of large distributed systems. The role is suited to candidates with experience solving large-scale systems problems who are interested in becoming experts in machine learning.
Responsibilities
- Implement low-latency, high-throughput sampling for large language models.
- Implement GPU kernels to adapt models to low-precision inference.
- Write custom load-balancing algorithms to optimize serving efficiency.
- Build quantitative models of system performance.
- Design and implement fault-tolerant distributed systems running with complex network topologies.
- Debug kernel-level network latency spikes in containerized environments.
- Collaborate through pair programming and research discussions.
Requirements
- Significant software engineering or machine learning experience, particularly at supercomputing scale.
- A results-oriented approach with flexibility and a focus on impact.
- Interest in learning more about machine learning research.
- Care for the societal impacts of technical work.
- A bachelor's degree or an equivalent combination of education, training, and experience.
- A field of study relevant to the role, as demonstrated through coursework, training, or professional experience.
Strong candidates may also have experience with:
- High-performance, large-scale machine learning systems.
- GPU or accelerator programming.
- Machine learning framework internals.
- Operating system internals.
- Language modeling with transformers.
Compensation
The annual salary range is $280,000–$850,000 USD. Applications are reviewed on a rolling basis.
Work Policy and Sponsorship
Staff are expected to work from one of Anthropic's offices at least 25% of the time, although some roles may require more office time. Anthropic sponsors visas and makes reasonable efforts to support visa applications, although sponsorship is not guaranteed for every role or candidate.
Benefits
Anthropic offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and an office space for collaboration.