Junior System Modeling: GPU-Accelerated AI Simulations

Unconventional, Inc.; United States; Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Mentorship by AI stack architects

Impact on AI compute stack

Unconventional problems to solve

Competitive equity and salary

Job summary

Unconventional AI is seeking a Junior Member of Technical Staff, System Modeling to develop GPU-accelerated simulators and integrate physics-based models into PyTorch. You will help link theory, algorithms, and device models across the stack.

You will collaborate with hardware and algorithms teams, contribute to reproducible experimentation, and push the boundaries of unconventional AI compute. A strong math and systems foundation is essential.

Qualifications

• BS/MS/PhD in CS or EE; strong computer architecture knowledge.
• Proficient in C++ and Python with performance-focused programming.
• Familiar with deep learning frameworks and ML concepts.
• Solid math background in linear algebra and calculus.
• Curiosity and willingness to challenge conventional software practices.

Responsibilities

• Contribute to GPU-accelerated simulators in PyTorch.
• Integrate physics-based models into PyTorch simulations.
• Maintain end-to-end simulation environment linking theory and hardware.
• Develop robust experiment tracking for reproducibility.
• Collaborate with Algorithms and Hardware teams to gather requirements.

Skills

C++

Python

Systems fundamentals

AI/ML exposure

Math intuition

First principles

Education

BS/MS/PhD in Computer Science or Electrical Engineering

Tools

CUDA

LLVM/MLIR

Triton

PyTorch

Job description

Unconventional AI is seeking a Junior Member of Technical Staff, System Modeling to develop GPU-accelerated simulators and integrate physics-based models into PyTorch. You will help link theory, algorithms, and device models across the stack.

You will collaborate with hardware and algorithms teams, contribute to reproducible experimentation, and push the boundaries of unconventional AI compute. A strong math and systems foundation is essential.