System Modeling (Computation)
Location
United States
Posted
7 days ago
Salary
0
Seniority
Mid Level
No structured requirement data.
Job Description
System Modeling (Computation)
Unconventional, Inc.
Role Description As a Member of Technical Staff, System Modeling (Computation), 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 large-scale, GPU-accelerated, high-fidelity differential equation solvers to enable development of ML on analog/unconventional hardware. - Developing physics-based models of device- and system-level behavior in unconventional compute, integrated with PyTorch. - Working with other teams to understand their needs for modeling and simulation to support high-level algorithm development 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. - Dynamical computation knowledge. - Experience with high-performance, customized GPU kernel development for numerical differential equation solvers (e.g., ODE, SDE). - Experience with DE solving algorithms designed for parallel architectures. - Experience with building effective neural network surrogate models for ODE/SDE/DDE solvers and functions. - Solid understanding of modern AI/ML architectures and training/inference workflows. - Strong experience implementing and debugging ML models in PyTorch (preferred). Preferred Qualifications - Basic familiarity of analog dynamic systems, including transient responses and nonidealities. - Strong Python engineering skills: modular design, testing, packaging, CI. - Experience with PyTorch internals and CUDA, Triton, or other GPU programming approaches. - Comfort with at least some of: JAX, NumPy, TensorFlow, Modal, HPC patterns. - Demonstrated ability to reason across multiple layers of the stack: algorithm, software, runtime, hardware. - Experience applying efficiency techniques (quantization, sparsity, pruning, distillation, kernel fusion, etc.). - Prior experience building or extending a serious simulation or modeling framework. - Comfort with approximations and tradeoffs. Benefits - A comprehensive package including best-in-class health benefits. - 401k matching. - Truly unlimited PTO. - Complimentary meals when working from our Palo Alto office.
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