AI Theory

• Exceptional technical ability in a quantitative field (e.g., Physics, Computer Science, Electrical Engineering, Applied Math, or a related discipline). An MS/PhD or equivalent research/project experience is strongly preferred.
• A "0-to-1" mindset. You have a demonstrated history of tackling complex, ambiguous R&D problems, often from a blank slate.
• Deep curiosity. You are comfortable diving into new domains, whether it's semiconductor physics, machine learning theory, or systems-level design.
• A creative and unconventional approach to problem-solving.
• Compute model technical staff may focus primarily on the above skillsets, or may be cross-disciplinary with hardware or AI/ML algorithms expertise.

Collaboration & Communication

• Cross-Functional Leadership: Excellent ability to translate complex technical concepts for diverse teams. You will act as a translator, discussing algorithmic/model trade-offs with ML/AI teams and eliciting hardware constraints and features from hardware engineering teams.
• Analytic Foundations: Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. Strong candidates are able to leverage the properties of (or impose constraints on) such systems to develop analytic insights.
• Practical Eye: The ability to leverageanalytic insights to build practical tools – metrics, algorithmic optimizations, and automated analyses – that can be used to study dynamical systems. Strong candidates are comfortable navigating the tradeoff space between theoretic purity and practicality to realize useful tooling.
• Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. Experience with libraries geared toward analysis, such as computer algebra libraries (e.g., sympy) also recommended.
• ML/AI Familiarity: Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. Experience with using high-level ML model frameworks, such as PyTorch and JAX.