Note: The job is a remote job and is open to candidates in USA. Unconventional AI is a pioneering company focused on redefining computing to overcome energy limitations in AI. They are seeking a Member of Technical Staff for System Modeling (Dynamic Systems Simulation) to develop high-performance models for complex dynamic systems, contributing to next-generation AI architectures.

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

Skills

• MS/PhD in Electrical Engineering, Computer Engineering, or closely related fields (e.g., Applied Physics with a specific focus on solid-state devices or VLSI), or BS with substantial evidence of equivalent research/engineering depth in circuit simulation
• Knowledge of Analog and Mixed-Signal circuit design: understand transistor level circuit design principles and modeling of nonidealities such as noise, mismatch, and process variations
• 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
• Dynamics & Differential Equations: A strong theoretical and practical grasp of linear and non-linear dynamics, state-space representations, and solving $dx/dt = f(x, u, t)$ within a machine learning context
• Stochastic Processes & Noise: Understanding how to model and mitigate noise in real-world systems, including experience with stochastic differential equations (SDEs) or Bayesian filtering
• Modeling & Simulation: Proven industry experience building high-fidelity circuit simulations that balance computational efficiency with physical accuracy
• Systems Engineering (Analog/Digital): Familiarity with hardware-level concepts like circuit dynamics, signal processing, or transfer functions is highly desirable to help ground our digital models in physical reality
• 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
• 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.)

Benefits

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

Company Overview