OpenAI의 Workload Networking 팀에서 대규모 모델 학습을 위한 Collective Communication 스택을 설계하고 구현할 엔지니어를 찾습니다. C++ 및 CUDA를 활용한 저수준 성능 최적화 역량이 필수적이며, RDMA 및 분산 알고리즘 경험자를 우대합니다. 샌프란시스코 기반의 하이브리드 근무 환경에서 세계 최고 수준의 슈퍼컴퓨터 네트워크를 설계하는 도전적인 역할을 수행하게 됩니다.
The Workload Networking team is responsible for the collective communication stack used in our largest training jobs. Using a combination of C++ and CUDA we work on novel collective communication techniques that enable efficient training of our flagship models on our largest custom built supercomputers.
The models we train are key ingredients to the AI research progress at OpenAI and the field as a whole, and we continually incorporate learnings from our entire research org into our training platform.
As a Software Engineer, Networking you will design and implement custom networking collectives that are tightly integrated into our training stack.
We’re looking for people who have a background in low level performance critical software. Experience with collective communication is a bonus.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
Collaborate closely with ML researchers to design and implement efficient collective operations in C++ and CUDA.
Ensure that our largest training jobs take full advantage of the different network transports used in our supercomputers.
Work on simulations to inform our future supercomputer network designs.
Have written distributed algorithms using RDMA in the past.
Are comfortable writing low level performance sensitive CPU and/or GPU code.
Are familiar with network simulation techniques.
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