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. Our work spans system software, networking, platform architecture, fleet-level monitoring, and performance optimization.

About the Role

We’re hiring an SW Engineer to enable production workloads and end-to-end testing on new platforms. This role will include creating new test harnesses and platform stress benchmarks, porting existing inference and training workloads to new, sometimes early-access, systems/hardware, analyzing performance and bottlenecks, and characterizing the end-to-end behavior of new systems (compute, comms, storage, control plane, and failure modes).

Key Responsibilities

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).

Work cross-functionally with vendors and internal stakeholders by producing clear bug reports, minimal repros, and prioritized issue lists.

Qualifications

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).

Strong profiling/debugging skills (e.g., Nsight, rocprof, perf, flamegraphs; ability to reason from traces/counters).

Preferred Skills

Experience building workload-shaped benchmarks and stress/fault tests that correlate to production behavior (not just synthetic loops or microbenchmarks).