Performance Modeling Lead
Leads a team building performance modeling frameworks to evaluate AI infrastructure tradeoffs in compute, memory, networking, and topology. Guides architectural decisions, influences vendor designs, and partners with ML/systems teams; requires deep AI workload and systems expertise.
About the job
Key Responsibilities
- Build and own a performance modeling framework/toolchain to evaluate AI systems across multiple levels of abstraction.
- Analyze and quantify architectural tradeoffs across compute, memory, networking, storage, and system topology.
- Develop performance models to guide decisions on: scale-up vs. scale-out architectures, interconnect and network design, memory hierarchy and system balance.
- Translate modeling outputs into clear recommendations for internal teams and external hardware vendors.
- Influence reference designs and vendor roadmaps through data-driven insights.
- Partner closely with machine learning, systems, and hardware teams to understand workload characteristics and requirements.
- Lead and grow a small team (2–3 engineers), setting technical direction and maintaining high standards for modeling rigor.
- Continuously improve modeling fidelity by validating against real system behavior and measurements.
Qualifications
- Experience owning or building performance modeling frameworks used to drive real system design decisions.
- Deep knowledge of AI/ML workloads, including training and/or inference at scale.
- Understand system-level tradeoffs across compute, memory, and networking in large-scale distributed systems.
- Comfortable working across abstraction layers—from workload behavior to hardware implementation.
- Experience using modeling (analytical or simulation) to inform architectural decisions.
- Can operate in ambiguous problem spaces and turn open-ended questions into structured analysis.
- Communicate clearly and influence both internal teams and external partners.
Preferred Skills
- Experience working with hardware vendors (ODM/JDM, silicon, networking).
- Background in data center infrastructure or hyperscale systems.
- Familiarity with accelerators (GPUs/ASICs) and interconnects (e.g., NVLink, InfiniBand, Ethernet).
- Experience influencing hardware roadmaps or reference architectures.
- Prior experience leading or mentoring engineers.
Skills
Performance Modeling, Ai/Ml Workloads, Gpus, Asics, Nvlink, InfiniBand, Ethernet, Distributed Systems, Scale-Up Architectures, Scale-Out Architectures
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