System Modeling (Performance Models)

Unconventional AI
Palo Alto
Workplace: HybridFull timeFunction: Software EngineeringEducation: mastersSkills: ["Problem-solving","Collaboration"]

Join a hands-on R&D team building simulation frameworks for evaluating unconventional physics-based computing systems for ML workloads. You’ll develop physics-based system models and GPU-accelerated ML simulations, enabling cross-layer design and rapid iteration across architectures, with strong emphasis on performance, power, and area estimation and collaboration with hardware and software teams.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
Unconventional AI
Unconventional AI
5 months ago

System Modeling (Performance Models)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 14 hours agoStatus: Live

Job Summary

Join a hands-on R&D team building simulation frameworks for evaluating unconventional physics-based computing systems for ML workloads. You’ll develop physics-based system models and GPU-accelerated ML simulations, enabling cross-layer design and rapid iteration across architectures, with strong emphasis on performance, power, and area estimation and collaboration with hardware and software teams.
Location: Palo Alto
Workplace: Hybrid
Employment Type: Full time
Job Function: Software Engineering

Key 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.
  • •Define and create 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.

Key Requirements

  • •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.
  • •Experience with tools and development for power profiling, modeling and simulation for AI workloads.
  • •Deep understanding of spatial architectures and data orchestration mechanisms.
  • •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.
  • •Familiar with CUDA, Triton, or other GPU programming approaches (writing custom kernels, understanding memory hierarchy, basic performance tuning).
Experience:AI hardwareML systemsGPU acceleration
Education:Master's in AI/ML, CS, Physics, Electrical Engineering
Skills:Problem-solvingCollaboration
Languages:English
Tech Stack:PythonPyTorchCUDATritonJAXNumPyTensorFlowModalMPINCCLDistributed trainingSciPyTimLoopAccelergyNeuroSimCIMLoopCACTI

Company Brief

Unconventional AI
Builds customizable AI copilots and fine-tuned large language model solutions to help teams automate workflows, surface actionable insights, and integrate generative AI into business processes across products and operations.
Industry: AI & Machine Learning
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Seed
Website