Production Workloads Engineer: Benchmark Enablement

OpenAI; United States; Remote; USD 140,000 - 220,000; Full time

Job summary

OpenAI is seeking an SW Engineer to enable production workloads and end-to-end testing on new platforms. You will port inference and training workloads to early-access systems, analyze performance bottlenecks, and characterize end-to-end behavior of compute, comms, storage and control planes.

You will develop test harnesses, CI-ready benchmarks, and ensure scalability with containerization, Kubernetes integration, and telemetry hooks, while collaborating with vendors and internal teams to drive

Qualifications

• Experience porting inference and training workloads to new platforms.

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

Job description

OpenAI is seeking an SW Engineer to enable production workloads and end-to-end testing on new platforms. You will port inference and training workloads to early-access systems, analyze performance bottlenecks, and characterize end-to-end behavior of compute, comms, storage and control planes.

You will develop test harnesses, CI-ready benchmarks, and ensure scalability with containerization, Kubernetes integration, and telemetry hooks, while collaborating with vendors and internal teams to drive