Software Engineer, Workload Enablement
About the Team The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models.
What this role actually needs.
Software Engineer, Workload Enablement at OpenAI in San Francisco. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. About the Team The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models.
Day-to-day expectations
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- Port and validate key inference and training workloads on new platforms/SKUs as they arrive; drive correctness, performance, and stability to an internal readiness bar.
- Build a suite of benchmarks and stress tests that capture real E2E behavior of our workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem, frontend, scale-up, and scale-out networking (including WAN traffic, NVlink and RDMA collectives), storage, thermals, and any other relevant parts.
- Deep-dive performance on distributed training/inference: Collective performance and tuning (across NCCL/RCCL and internal libraries)
- Overlap of compute/communication, kernel-level bottlenecks, memory bandwidth and scheduling effects
- Create repeatable test harnesses that run in CI / lab environments and produce actionable outputs (pass/fail, performance score, regression detection).
- Partner with systems + fleet bring-up engineers to ensure the platform is not only stable and performant, but also operationally usable and scalable (containerization, K8s integration, telemetry hooks, failure triage loops).
What a strong candidate brings
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- BS in CS/EE (or equivalent practical experience).
- 5+ years in one or more of: ML systems, performance engineering, distributed systems, or HPC.
- Strong hands-on experience with: PyTorch and modern LLM training/inference stacks
- Large-scale distributed training concepts (data/model/pipeline parallel, collective comms)
- Experience with RDMA and debugging/optimizing comms libraries (NCCL or RCCL) and their interaction with hardware/network
- Proficiency in Python plus comfort reading/writing performance-critical code (C++/CUDA/HIP is a plus).
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Next moves
- Tailor your resume around ai and llm instead of sending a generic application.
- Use the first two bullets of your application to connect your background directly to software engineer, workload enablement is a high-signal hybrid role in san francisco, and it is most realistic for united states residents.
- Open the role quickly if it fits and bookmark three similar jobs before you leave the page.
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- $293K - $455K is visible, so calibrate your application around the posted range.
- Use united states residents as part of your positioning so the recruiter does not have to infer it.
- Show concrete examples of succeeding in hybrid environments.
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