About Unconventional AI
We are rethinking the foundations of the computer to optimize energy efficiency for AI. Founded by pioneers in the field - including Naveen Rao (Nervana, MosaicML) and Michael Carbin (MIT, MosaicML) - we are building a new computational substrate that interfaces directly with the physics of silicon to achieve biology-scale efficiency. We recently raised $475M in seed funding to turn this vision into reality.
As a Junior Member of Technical Staff, System Modeling, you will work closely with senior engineers to contribute to the development of our multi-disciplinary simulation frameworks. You will assist the hands-on R&D team in building simulation environments that enable rapid iteration and testing across all layers of our unconventional physics-based computing systems for machine learning workloads. Your work will focus on integrating physics-based models, developing GPU-accelerated simulations, and supporting the cross-layer system integration necessary for "Extreme co-design".
Key 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.
What We’re Looking For
Strong Systems Foundation: A BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field. You should have a deep understanding of computer architecture and operating systems.
Coding Proficiency: Strong skills in C++ and Python. You should be comfortable writing performance-critical code.
AI/ML Exposure: Basic familiarity with the internals of deep learning frameworks (e.g., how a PyTorch graph is executed) and common model architectures.
Mathematical Intuition: A solid grasp of linear algebra and calculus, which are essential for understanding both neural dynamics and hardware optimizations.
First Principles Mindset: You enjoy digging into "why" things work (or don't) and aren't afraid to challenge conventional software "best practices" to find a more efficient path.
Bonus Points
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.
Why Join Us?
Mentorship: Learn directly from the architects who built the modern AI stack at companies like Intel, Databricks, and NVIDIA.
Impact: You won't be a small cog in a giant machine. You will be helping build the machine itself.
Unconventional Problems: Work on challenges that don't have a StackOverflow answer—you’ll be defining the future of AI compute.
Competitive Package: Significant equity and competitive salary at a well-funded, high-growth startup.
Unconventional AI is rethinking the foundations of computing to optimize energy efficiency for AI workloads. Founded by pioneers including Naveen Rao (Nervana, MosaicML) and Michael Carbin (MIT, MosaicML), the company is building a new computational substrate that interfaces directly with the physics of silicon to achieve biology-scale efficiency. They have recently raised $475M in seed funding to develop this physics-based computing system designed specifically for machine learning applications. The company's approach involves developing multi-disciplinary simulation frameworks that enable rapid iteration and testing across all layers of their unconventional computing systems.
