Scan your resume against ATS criteria for this System Modeling (Performance Models) role at Unconventionalinc.
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. We're doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.
As a Member of Technical Staff, System Modeling (Performance Models), 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, and the team we’re building reflects that. The role involves development of physics-based system models, GPU-accelerated ML system simulations, and cross-layer system integration. You don’t need to be an expert in all of these, but you have to be very strong in at least, and solid in the rest.
You will be responsible for or more of the following tasks:
for novel AI acceleration system architectures to enable rapid design space exploration.
across candidate architectures and existing state-of-art implementations.
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 of spatial architectures and data orchestration mechanisms
Deep 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, etc.
Familiar with different existing systolic array accelerator architectures for AI/ML workloads
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.
We are looking for well-rounded candidates. While the minimum qualifications focus on the core modeling and ML expertise, candidates who possess the following qualities will be the most impactful.
Dynamic systems knowledge
Basic familiarity of 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 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.
Systems thinking
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.).
Modeling / simulation mindset
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 physics.
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.
How to apply for System Modeling (Performance Models) at Unconventionalinc?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
Salary details will be discussed during the interview.
What experience is required?
50+ years of experience is required.
Is this position still open?
Yes, currently active and accepting applications.
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System Modeling (Performance Models)
Unconventionalinc · Mountain View
