Performance Characterization Engineer
About The Position
Join our team as a Senior Performance Characterization Engineer and take the lead in illuminating the performance landscape of our cutting-edge ML accelerator. We are seeking a highly skilled engineer to design and develop a sophisticated performance analysis tool, tailored specifically for Sohu. You will be instrumental in creating the essential tooling that enables our ML engineers and customers to understand workload behavior, identify performance bottlenecks, and unlock the full potential of Sohu accelerating the most demanding ML applications in the world. This is a unique opportunity to shape performance analysis for novel hardware from the ground up.
Requirements
- Strong proficiency in C/C++ and Python.
- Deep understanding of computer architecture (CPU, GPU, accelerators), memory hierarchies (caches, DRAM), and interconnects (especially PCIe).
- Proven experience in low-level performance analysis, profiling, and bottleneck identification on complex hardware systems (GPUs, CPUs, FPGAs, or custom ASICs).
- Experience with performance analysis tools (e.g., NVIDIA Nsight, AMD uProf, Intel VTune, perf, Tracy, ETW).
- Solid understanding of operating system internals (Linux preferred), including scheduling, memory management, and driver interaction.
- Experience working close to hardware, potentially reading performance counters or interacting directly with device drivers.
Nice To Haves
- Direct experience developing performance analysis or debugging tools.
- Experience with ML accelerator architectures (GPUs, TPUs, etc.).
- Experience with kernel-mode driver development (Linux or Windows).
- Understanding of compiler internals, code generation, and optimization.
- In-depth knowledge of the PCIe protocol and analysis tools (PCIe analyzers).
- Experience with firmware or embedded systems development.
- Experience with hardware description languages (Verilog, VHDL) or hardware verification.
Responsibilities
- Tool Architecture & Design: Lead the design and architecture of a comprehensive performance analysis suite, including data collection mechanisms, data processing pipelines, analysis engines, and user interfaces (CLI and/or GUI).
- Low-Level Data Collection: Develop robust methods to capture performance data directly from our custom ML accelerator hardware (e.g., hardware performance counters, execution unit status, memory access patterns) via driver interfaces or other mechanisms.
- Host & System Tracing: Implement tracing for host-side API calls (runtime libraries, driver interactions) and system-level events (CPU activity, PCIe traffic, memory usage, network contention) related to Sohu workloads.
- Data Correlation & Synchronization: Design and implement techniques to accurately correlate performance events across the host CPU, device driver, PCIe bus, and multiple accelerators, ensuring precise time synchronization.
- Performance Analysis Engine: Build analysis modules to automatically interpret collected trace and counter data, identifying key performance limiters (e.g., compute-bound, memory bandwidth-bound, latency-bound, PCIe-bound, specific hardware bottlenecks).
- Visualization & Reporting: Develop intuitive visualizations (timelines, dependency graphs, resource utilization charts, statistical summaries) to clearly communicate performance characteristics and bottlenecks to users.
- Collaboration & Support: Work closely with hardware architects, firmware engineers, driver developers, compiler engineers, and ML application engineers to understand their needs, define tool requirements, and provide expert guidance on performance analysis and optimization using the tool.
Benefits
- Full medical, dental, and vision packages, with generous premium coverage
- Housing subsidy of $2,000/month for those living within walking distance of the office
- Daily lunch and dinner in our office
- Relocation support for those moving to West San Jose
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
Number of Employees
101-250 employees
