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Software Engineer, Workload Enablement

OpenAI

Software Engineer, Workload Enablement is a still-open role at OpenAI on Jobrow. Location: Hybrid · San Francisco · California · United States. Listed pay: $293k–$385k. Sourced from the company's public Ashby board. First seen 2 months ago; last confirmed 9 days ago. Apply on the employer ATS — Jobrow is not the employer.

Pay
$293k–$385k
Workplace
Hybrid
Location
San Francisco · California · United States
First seen
2 months ago
Last seen
9 days ago
Board
Ashby
Apply

About this role

About the Team The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure.

  • Function: Engineering
  • Seniority: mid
  • pytorch

Location & pay

Hybrid · San Francisco · California · United States Listed pay on Jobrow: $293k–$385k.

Source

Jobrow reads the public Ashby board for OpenAI. First seen 2 months ago; last seen 9 days ago. We are not the employer — apply only on the company ATS.

Posting

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 R

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