System Modeling (Computation)

Unconventional AI; Palo Alto (CA); On-site; USD 170,000 - 260,000; Full time

Benefits offered by this job

Health benefits

401k matching

Unlimited PTO

Complimentary meals at Palo Alto офис

Job summary

Unconventional is seeking a Member of Technical Staff, System Modeling (Computation) in Palo Alto to build simulation frameworks for physics‑based computing systems powering ML workloads. You will work on extreme co‑design, bridging physics and ML, and contribute across multiple layers from algorithms to hardware.

Candidates have graduate degrees in AI/ML, CS, physics or related fields, experience with GPU‑accelerated ML simulations, and a track record delivering high‑performance numerical

Qualifications

• MS/PhD in a quantitative field or BS with substantial research depth.
• Scientific ML and numerical methods expertise.
• Deep expertise in numerical solvers (ODE, SDE, DDE) on parallel GPUs.
• Experience with high‑performance GPU kernel development and memory optimization.
• Experience building neural network surrogate models for complex dynamical systems.
• Solid understanding of modern AI/ML architectures and workflows.

Responsibilities

• Architect foundational solvers: large‑scale, GPU‑accelerated numerics and visualization tools.
• Bridge physics and ML: surrogate models and cross‑layer optimization via autodiff.
• Collaborate with hardware and algorithm teams to support simulation needs.

Skills

SciML & numerical methods

ML & systems fluency

PyTorch

GPU kernel development

Python engineering

Cross-layer optimization

Education

MS/PhD or BS with evidence

Tools

JAX

PyTorch

CUDA/Triton kernels

Job description

About Unconventional

Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation—a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale. At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient, exploiting the rich physics of semiconductors and mapping neural networks directly to device physics rather than relying on layers of inefficient abstraction.

The Role

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, requiring strong expertise in at least one of physics‑based system models, GPU‑accelerated ML system simulations, or cross‑layer system integration.

Responsibilities

• Architecting Foundational Solvers: Building large‑scale, GPU‑accelerated, high‑fidelity numerical differential equation solvers (ODE, SDE, CDE, PDE) and tools that enable rapid iteration, multiple architectures, and rich metrics/visualization, leveraging frameworks such as JAX, PyTorch, or custom CUDA/Triton kernels.
• Bridging Physics and Machine Learning: Developing physics‑based surrogate models of device‑ and system‑level behavior in unconventional compute, creating clean, composable abstractions that expose algorithm–hardware tradeoffs and enable cross‑layer optimization via end‑to‑end autodiff.
• 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.

Minimum Qualifications

• Education
  • 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.
• Scientific Machine Learning (SciML) & Numerical Methods (replaces Dynamical computation knowledge)
• 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.
• Experience building effective neural network surrogate models (e.g., Neural ODEs) for complex dynamical systems.
• ML and systems fluency
  • 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

• Software Engineering & API Craftsmanship
  • Exceptional Python engineering skills with a passion for Developer Experience (DX), elegant API design, strong typing, and composability.
  • Experience with compiler‑friendly ML paradigms and internals (e.g., JAX vmap/pmap/jit, PyTorch autograd/torch.compile, custom XLA or Triton kernels).
  • A track record of building open‑source tools, scientific libraries, or serious simulation/modeling frameworks from scratch.
• 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).
  • Experience applying efficiency techniques natively within modeling frameworks (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 Impact: Shape the company's future as a foundational team member. Enjoy massive ownership and an outsized opportunity to drive change.
• The Challenge: Dive into deeply complex, intellectually stimulating, and unsolved problems at the cutting edge of multiple, converging fields.
• The Perks: A comprehensive package including best‑in‑class health benefits, 401k matching, truly unlimited PTO, and complimentary meals when working from our Palo Alto office.