[Remote] System Modeling (Dynamic Systems Simulation)

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.ResponsibilitiesYou 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 behaviorSkillsMS/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 simulationKnowledge of Analog and Mixed-Signal circuit design: understand transistor level circuit design principles and modeling of nonidealities such as noise, mismatch, and process variationsAdvanced 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 processesDynamics & 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 contextStochastic Processes & Noise: Understanding how to model and mitigate noise in real-world systems, including experience with stochastic differential equations (SDEs) or Bayesian filteringModeling & Simulation: Proven industry experience building high-fidelity circuit simulations that balance computational efficiency with physical accuracySystems 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 realitySolid understanding of modern AI/ML architectures and training/inference workflowsStrong experience implementing and debugging ML models in PyTorch (preferred) or similar, with practical experience profiling, optimizing, and stabilizing non-trivial large-scale ML systemsStrong Python engineering skills: modular design, testing, packaging, CIExperience with PyTorch internals: autograd, custom modules, low-level ops; familiarity with torch.compile or similar graph capture/compile flowsExperience 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), SciPyDemonstrated ability to reason across multiple layers of the stack: algorithm, software, runtime, hardwareAble to connect model architecture choices to system performance implications: memory bandwidth, communication patterns, latency, energy, and numerical issuesExperience applying at least some efficiency techniques (quantization, sparsity, pruning, distillation, kernel fusion, etc.)BenefitsBest-in-class health benefits401k matchingTruly unlimited PTOComplimentary meals when working from our Palo Alto officeCompany OverviewUnconventional AI rethinks computer foundations to optimize energy efficiency for AI. It was founded in 2025, and is headquartered in San Francisco, California, USA, with a workforce of 11-50 employees. Its website is https://unconv.ai.