Workload Porting & Performance Engineer
OpenAI - San Francisco, California, United States, Seattle
Posted Apr 20, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- 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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- 401(k) match
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Schedule
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- Weekend work
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Application
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About this role
Workload Porting & Performance Engineer San Francisco, California, United States, Seattle About the Team OpenAI's Infrastructure organization builds and evaluates the systems that power advanced AI workloads. We work closely with hardware, modeling, and architecture teams to ensure that new platforms deliver real-world performance aligned with workload needs. Our team focuses on understanding workload behavior across evolving hardware platforms-bridging the gap between theoretical capability and observed system performance. About the Role We are seeking a Workload Porting & Performance Engineer to evaluate new hardware platforms by porting benchmarks and real-world workloads, analyzing performance, and identifying system bottlenecks. In this role, you will bring up workloads on new systems, characterize performance behavior, and adapt workloads to better utilize hardware capabilities. You will play a critical role in validating new platforms and ensuring that performance aligns with expectations across compute, memory, and networking subsystems. This role requires strong hands-on experience with performance analysis, workload optimization, and system-level debugging across hardware and software boundaries. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance. Key Responsibilities - Port and enable benchmarks and real-world workloads on new hardware platforms. - Evaluate system performance across compute, memory, storage, and networking subsystems. - Identify and analyze performance bottlenecks and inefficiencies. - Adapt and optimize workloads to better utilize hardware capabilities. - Develop and run performance experiments and profiling workflows. - Compare expected vs. observed performance and provide feedback to: -
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