[Remote] System Modeling (Performance Models)

Note: The job is a remote job and is open to candidates in USA. Unconventional AI is focused on redefining computing to address the energy limitations of AI on a global scale. They are seeking a Member of Technical Staff for System Modeling (Performance Models) to develop physics-based system models and simulation frameworks for machine learning workloads, supporting innovative AI acceleration architectures.

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

Skills

• 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
• 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

• Best-in-class health benefits
• 401k matching
• Truly unlimited PTO
• Complimentary meals when working from our Palo Alto office

Company Overview