About the RoleWe 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:
- hardware architecture teams
- performance modeling teams
- system and software engineers.
- Debug issues across the stack, including software, runtime, and hardware interactions.
- Provide actionable insights to guide platform readiness and deployment decisions.
Qualifications- Experience with performance analysis, benchmarking, or workload optimization.
- Strong understanding of system architecture, including CPU/GPU, memory, and I/O subsystems.
- Experience porting or adapting workloads across different hardware platforms.
- Familiarity with profiling tools and performance debugging techniques.
- Ability to identify root causes of performance issues across hardware/software boundaries.
- Experience working in large-scale or distributed system environments.
Preferred Skills- Experience with AI/ML workloads, including training or inference systems.
- Familiarity with GPU or accelerator-based systems.
- Experience working with low-level performance tools (profilers, tracing, microbenchmarks).
- Background in systems software, compilers, or runtime optimization.
- Experience collaborating with hardware and architecture teams on performance validation.
