Unconventionalinc
System Modeling (Computation)
Palo Alto, CA I US Remote
Sponsorship not specifiedDetected 40 days ago
PythonAPI DevelopmentMachine LearningPyTorchElectrical EngineeringResearchCommunicationCollaboration
About the role
- 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
- Architecting Foundational Solvers: Building large-scale, GPU-accelerated, high-fidelity numerical differential equation solvers (ODE, SDE, CDE, PDE).
- You will build tools that enable rapid iteration, multiple architectures, and rich metrics/visualization, leveraging frameworks best suited for scientific ML (e.g., JAX, PyTorch, or custom CUDA/Triton kernels).
- Extreme Co-Design & Collaboration: Working closely with hardware and algorithm teams to understand their simulation needs, supporting everything from high-level algorithm development to the low-level verification of novel, analog hardware.
- Enjoy massive ownership and an outsized opportunity to drive change.
- You will create clean, composable abstractions that expose algorithm-hardware tradeoffs and enable cross-layer optimization via end-to-end autodiff.
Requirements
- Deep expertise in numerical differential equation solvers (e.g., ODE, SDE, DDE) and their implementation on parallel architectures (e.g., Rosenbrock methods, Euler-Maruyama, adjoint methods, implicit solvers).
- Experience with high-performance, customized GPU kernel development for numerical methods, including GPU memory optimization and multi-GPU scaling.
Nice to have
- While the minimum qualifications focus on the core modeling and ML expertise, candidates who possess the following qualities will be the most impactful.
- Software Engineering & API Craftsmanship
- Experience with compiler-friendly ML paradigms and internals (e.g., JAX vmap/pmap/jit, PyTorch autograd/torch.compile, custom XLA or Triton kernels).
- Dynamic Systems Knowledge
- Familiarity with analog dynamic systems, including transient responses, and nonidealities such as nonlinearity, quantization, random noise, and feedback/stability.
- Systems Thinking
- Demonstrated ability to reason across multiple layers of the stack: algorithm, software, compiler, runtime, and hardware.
- Able to cleanly connect model architecture choices to system performance implications (memory bandwidth, communication patterns, latency, energy, and numerical stability).
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.
- Bridging Physics and Machine Learning: Developing physics-based surrogate models of device- and system-level behavior in unconventional compute.
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