Junior System Modeling

Summary

Unconventional AI is rethinking the foundations of computing to optimize energy efficiency for AI. As a Junior Member of Technical Staff, System Modeling, you will work closely with senior engineers to develop multi-disciplinary simulation frameworks and support the integration of physics-based models for machine learning workloads.

Responsibilities

• Contribute to the implementation and optimization of GPU-accelerated simulators for ML on analog/unconventional hardware, focusing on specific modules and features within PyTorch
• Assist in integrating physics-based device and system models into the PyTorch simulation environment to help expose early algorithm–hardware tradeoffs and enable cross-layer optimization
• Support the maintenance and extension of the unified end-to-end simulation environment, helping to link theory, algorithms, and device models, and ensuring alignment between high-level and near-physical simulators
• Help implement and adhere to robust experiment tracking protocols to ensure simulation results, configurations, and non-idealities are reproducible and auditable
• Collaborate with Algorithms and Hardware teams to gather requirements and ensure the modeling environment meets their needs for high-level algorithm development and lower-level hardware verification

Skills

• A BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field
• A deep understanding of computer architecture and operating systems
• Strong skills in C++ and Python
• Basic familiarity with the internals of deep learning frameworks (e.g., how a PyTorch graph is executed) and common model architectures
• A solid grasp of linear algebra and calculus
• A first principles mindset
• Experience with compilers (LLVM, MLIR) or domain-specific languages like Triton
• Exposure to GPU programming (CUDA) or other hardware accelerators
• Prior research or internship experience in high-performance computing (HPC) or neuromorphic systems
• Contributions to open-source AI or systems software projects

Qualifications

Must Haves

• A BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field
• A deep understanding of computer architecture and operating systems
• Strong skills in C++ and Python
• Basic familiarity with the internals of deep learning frameworks (e.g., how a PyTorch graph is executed) and common model architectures
• A solid grasp of linear algebra and calculus
• A first principles mindset

Nice to Haves

• Experience with compilers (LLVM, MLIR) or domain-specific languages like Triton
• Exposure to GPU programming (CUDA) or other hardware accelerators
• Prior research or internship experience in high-performance computing (HPC) or neuromorphic systems
• Contributions to open-source AI or systems software projects

Benefits

• Mentorship: Learn directly from the architects who built the modern AI stack at companies like Intel, Databricks, and NVIDIA.
• Significant equity and competitive salary at a well-funded, high-growth startup.