[Remote] System Modeling (Performance Models)

Note: The job is a remote job and is open to candidates in USA. Unconventional AI is focused on redefining computing to address the energy limitations of AI on a global scale. They are seeking a Member of Technical Staff for System Modeling (Performance Models) to develop physics-based system models and simulation frameworks for machine learning workloads, supporting innovative AI acceleration architectures.ResponsibilitiesBuilding extensible and composable high-fidelity power, performance and area estimation tools for novel AI acceleration system architectures to enable rapid design space explorationDefine and create comparative analyses across candidate architectures and existing state-of-art implementationsWorking with other teams to understand their needs for such modeling and simulation to support high level system design as well as lower level verification of hardwareSkillsMS/PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or BS with substantial, clear evidence of equivalent research/engineering depthExperience with tools and development for power profiling, modeling and simulation for AI workloadsDeep understanding of spatial architectures and data orchestration mechanismsDeep understanding of different dataflow strategies and their tradeoffs, e.g. Weight-Stationary (WS), Output-Stationary (OS), Input-Stationary (IS) and Row-Stationary (RS)Familiar with (OSS) tools for hardware accelerator design: TimLoop, Accelergy, NeuroSim, CIMLoop, CACTI, etcFamiliar with different existing systolic array accelerator architectures for AI/ML workloadsSolid 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 systemsBasic familiarity of analog dynamic systems, including transient responses, nonidealities such as nonlinearity, quantization, random noise, and feedback/stabilityStrong 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.)Prior experience building or extending a serious simulation or modeling framework (could be ML systems, physics, circuits, or other technical domains)Comfort with approximations and tradeoffs: you know when to use a simple model and when you need something closer to the physicsBenefitsBest-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.