About the RoleWe are seeking Performance Modeling Engineers to develop and apply modeling tools that evaluate AI system performance and inform architectural decisions.
In this role, you will work closely with the Performance Modeling Lead and partner teams to analyze system behavior, run simulations or analytical models, and help quantify tradeoffs across compute, memory, networking, and storage. You will contribute to building modeling frameworks and applying them to real-world questions that impact system design and vendor decisions.
This role is well-suited for engineers with strong software or modeling backgrounds who are interested in developing deeper expertise in system architecture and AI infrastructure.
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- Develop and maintain performance modeling tools and frameworks.
- Build models to evaluate system behavior across:
- compute, memory, and interconnect subsystems
- distributed system scaling and bottlenecks.
- Run simulations and analytical models to support architectural tradeoff analysis.
- Collaborate with performance modeling lead and system architects to answer forward-looking design questions.
- Analyze and interpret modeling outputs, translating results into actionable insights.
- Validate models against real system measurements and workload behavior.
- Contribute to improving modeling fidelity, usability, and scalability.
Qualifications- Strong software engineering or modeling background (e.g., simulation, systems modeling, or performance analysis).
- Familiarity with system architecture fundamentals (compute, memory, networking).
- Experience with programming and building technical tools or frameworks.
- Ability to reason about performance bottlenecks and scaling behavior.
- Strong analytical skills and comfort working with quantitative models.
- Ability to collaborate across teams and learn new system domains quickly.
Preferred Skills- Exposure to AI/ML workloads or distributed systems.
- Experience with simulation tools, performance modeling, or systems analysis.
- Familiarity with data center infrastructure or large-scale systems.
- Experience working with performance data, benchmarking, or profiling tools.
- Interest in system architecture and hardware/software co-design.
