Unconventionalinc

Unconventionalinc

System Modeling (Dynamic Systems Simulation)

Palo Alto, CA I US Remote

Sponsorship not specifiedDetected 34 days ago
PythonMachine LearningTensorFlowPyTorchNumPySystems EngineeringElectrical EngineeringSignal ProcessingResearchCommunication

About the role

  • We need tools that simulate complex analog and mixed-signal systems millions of times faster than traditional circuit simulators, without losing the critical dynamics that govern system behavior.
  • The role involves development of physics-based system models, GPU-accelerated ML system simulations, and 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

  • You will be responsible for developing high-performance PyTorch or JAX components that model complex, time-varying circuit-based dynamic systems.
  • Your work will directly enable next-generation AI architectures, requiring a holistic approach involving everything from high-level neural network design down to the fundamental differential equations that govern system behavior.
  • Enjoy massive ownership and an outsized opportunity to drive change.

Requirements

  • Advanced Neural Modeling (PyTorch or JAX): proficiency in PyTorch or JAX, specifically in building custom autograd functions and integrating numerical solvers (e.g., Neural ODEs) to represent dynamic processes.
  • Stochastic Processes & Noise: Understanding how to model and mitigate noise in real-world systems, including experience with stochastic differential equations (SDEs) or Bayesian filtering.

Nice to have

  • While the minimum qualifications focus on the core modeling and ML expertise, candidates who possess the following qualifications will be the most impactful.
  • Software engineering
  • 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).
  • Able to connect model architecture choices to system performance implications: memory bandwidth, communication patterns, latency, energy, and numerical issues.

Skills

  • modular design, testing, packaging, CI.
  • Comfort with at least some of: JAX, NumPy, TensorFlow, Modal, HPC patterns (MPI, NCCL, distributed training), SciPy.
  • Systems thinking
  • Demonstrated ability to reason across multiple layers of the stack: algorithm, software, runtime, hardware.
  • Experience applying at least some efficiency techniques (quantization, sparsity, pruning, distillation, kernel fusion, etc.).
  • Why Join Us?
  • The Mission: Redefine computing for the next 50 years by solving the fundamental energy limitation of AI at a global scale.
  • The

Benefits

  • A comprehensive package including best-in-class health benefits, 401k matching, truly unlimited PTO, and complimentary meals when working from our Palo Alto office.
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