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Software Engineer, Workload Enablement

OpenAI - San Francisco, California, United States, Seattle

Posted Mar 28, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
Not verified checked Jun 7, 2026
Salary
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401(k) match
Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)
$116,543 U.S. median for this role
Projected growth (BLS Employment Projections)
+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Engineering From the posting source checked Jun 20, 2026
Seniority
Mid From the posting source checked Jun 20, 2026

Schedule

Shift type
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Weekend work
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Company

Company stage
Public-company From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
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Deadline
Not stated

Where they hire

State eligibility is not yet verified.

About this role

Software Engineer, Workload Enablement San Francisco, California, United States, Seattle 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

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