Software Engineer, Workload Enablement
OpenAI · San Francisco · Seattle
CHECKED 1H AGO
Frontier labremotesenior$293k–$385k
At a glance
Highlights
- Enable production workloads and end-to-end testing on new AI platforms.
- Port inference and training workloads to early-access systems and hardware.
- Build system-wide benchmarks capturing real end-to-end workload behavior.
What they offer
- Equity
Ideal candidate
Systems-minded performance engineer with deep distributed systems experience who loves bringing up early hardware, digging into bottlenecks, and turning messy research code into reliable, repeatable infrastructure.
Requirements
- BS in CS/EE or equivalent practical experience
- 5+ years in ML systems, performance engineering, distributed systems, or HPC
- Hands-on experience with PyTorch and modern LLM training/inference stacks
- Understanding of large-scale distributed training including data/model/pipeline parallelism
- Experience debugging and optimizing RDMA comms libraries like NCCL or RCCL
- Proficiency in Python with ability to read performance-critical C++/CUDA/HIP code
- Strong profiling and debugging skills using traces, counters, and performance tools
Lab profile
About OpenAI
Builds safe frontier AI models and products like ChatGPT for broad benefit.
What they build
- ChatGPT
- OpenAI API
- Frontier AI models
Known for
- Frontier-scale distributed ML training
- Massive GPU fleet and data center operations
- Exabyte-scale data lakes and streaming
- AI alignment and safety research
Key facts
- Operates one of the world's largest GPU fleets