[Hiring] System Modeling (Performance Models) @Unconventional

Role Description

As a Member of Technical Staff, System Modeling (Performance Models), you will be part of a hands-on R&D team building simulation frameworks that enable evaluation and rapid iteration across all layers of unconventional physics-based computing systems for machine learning workloads. “Extreme co-design” is our guiding principle.

System Modeling is a multi-disciplinary effort, and the team we’re building reflects that. The role involves:

• Development of physics-based system models
• GPU-accelerated ML system simulations
• Cross-layer system integration

You don’t need to be an expert in all of these, but you have to be very strong in at least one, and solid in the rest.

Responsibilities

• Building extensible and composable high-fidelity power, performance and area estimation tools for novel AI acceleration system architectures to enable rapid design space exploration.
• Define and create comparative analyses across candidate architectures and existing state-of-art implementations.
• Working with other teams to understand their needs for such modeling and simulation to support high level system design as well as lower level verification of hardware.

Qualifications

• MS/PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or BS with substantial, clear evidence of equivalent research/engineering depth.
• Experience with tools and development for power profiling, modeling and simulation for AI workloads.
• Deep understanding of spatial architectures and data orchestration mechanisms.
• Deep understanding of different dataflow strategies and their tradeoffs, e.g. Weight-Stationary (WS), Output-Stationary (OS), Input-Stationary (IS) and Row-Stationary (RS).
• Familiar with (OSS) tools for hardware accelerator design: TimLoop, Accelergy, NeuroSim, CIMLoop, CACTI, etc.
• Familiar with different existing systolic array accelerator architectures for AI/ML workloads.
• Solid understanding of modern AI/ML architectures and training/inference workflows.
• Strong experience implementing and debugging ML models in PyTorch (preferred) or similar, with practical experience profiling, optimizing, and stabilizing non-trivial large-scale ML systems.

Preferred Qualifications

• Basic familiarity of analog dynamic systems, including transient responses, nonidealities such as nonlinearity, quantization, random noise, and feedback/stability.
• Strong Python engineering skills: modular design, testing, packaging, CI.
• Experience with PyTorch internals: autograd, custom modules, low-level ops; familiarity with torch.compile or similar graph capture/compile flows.
• Experience with CUDA, Triton, or other GPU programming approaches (writing custom kernels, understanding memory hierarchy, basic performance tuning).
• Comfort with at least some of: JAX, NumPy, TensorFlow, Modal, HPC patterns (MPI, NCCL, distributed training), SciPy.
• Demonstrated ability to reason across multiple layers of the stack: algorithm, software, runtime, hardware.
• Able to connect model architecture choices to system performance implications: memory bandwidth, communication patterns, latency, energy, and numerical issues.
• Experience applying at least some efficiency techniques (quantization, sparsity, pruning, distillation, kernel fusion, etc.).
• Prior experience building or extending a serious simulation or modeling framework (could be ML systems, physics, circuits, or other technical domains).
• Comfort with approximations and tradeoffs: you know when to use a simple model and when you need something closer to the physics.

Benefits

• A comprehensive package including best-in-class health benefits
• 401k matching
• Truly unlimited PTO
• Complimentary meals when working from our Palo Alto office