Software Engineer, Collective Communication
About the Team 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.
What this role actually needs.
About the Team 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. Responsibilities: - 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. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
Day-to-day expectations
OpenAI lists these responsibilities for the Software Engineer, Collective Communication role.
- 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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Source: jobs.ashbyhq.com · Source ID: 340c0c22-8d8f-4232-b17e-f642b64c25c3 · Confidence: 97/100 · Last checked: May 7, 2026
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