System Modeling (Performance Models)

About the role

Since 2022, AI has transformed industries ranging from education and software development to consumer behaviors. This transformation has highlighted a critical need for more efficient computation, particularly at a global scale. At Unconventional, our mission is to address this challenge by rethinking computing to build a new foundation for AI that is 1000x more energy-efficient. We achieve this through exploiting semiconductor physics to map neural networks directly onto device physics, bypassing inefficient layers of abstraction.

Responsibilities

• Building extensible and composable high-fidelity power, performance and area estimation tools for novel AI acceleration system architectures to enable rapid design space exploration.
• Defining and creating comparative analyses across candidate architectures and existing state-of-the-art implementations.
• Working 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 hardware.

Requirements

• 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.
• Performance Modeling Knowledge: Experience with tools and development for power profiling, modeling, and simulation for AI workloads.
• Deep Understanding: Deep understanding of spatial architectures and data orchestration mechanisms, different dataflow strategies, and their tradeoffs.
• Familiarity with Tools: Familiarity with OSS tools for hardware accelerator design like TimLoop, Accelergy, NeuroSim, CIMLoop, CACTI, etc.
• 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).

Qualifications

• Dynamic Systems Knowledge: Basic familiarity with analog dynamic systems, including transient responses, nonidealities such as nonlinearity, quantization, random noise, and feedback/stability.
• Software Engineering: Strong Python engineering skills: modular design, testing, packaging, CI.
• Experience: Experience with PyTorch internals: autograd, custom modules, low-level ops; familiarity with torch.compile or similar graph capture/compile flows.
• CUDA, Triton, or Other GPU Programming: Experience with CUDA, Triton, or other GPU programming approaches (writing custom kernels, understanding memory hierarchy, basic performance tuning).
• Systems Thinking: Demonstrated ability to reason across multiple layers of the stack: algorithm, software, runtime, hardware.
• Modeling / Simulation Mindset: Prior experience building or extending a serious simulation or modeling framework (could be ML systems, physics, circuits, or other technical domains).
• Efficiency Techniques: Comfort with at least some efficiency techniques (quantization, sparsity, pruning, distillation, kernel fusion, etc.).

Skills

• Dynamic Systems Knowledge
• Software Engineering
• Experience with PyTorch Internals
• CUDA, Triton, or Other GPU Programming
• Systems Thinking
• Modeling / Simulation Mindset
• Efficiency Techniques

Benefits

• Comprehensive Package: Best-in-class health benefits, 401k matching, truly unlimited PTO, and complimentary meals when working from our Palo Alto office.

Pay

Competitive compensation package based on experience and qualifications.

Schedule

Full-time position with flexible working hours.