AI Sr. Principal Engineer

• Teaching machines to represent and understand structured engineering artifacts (graphs, netlists, layouts, physical models).
• Building learning and optimization systems that search vast design spaces and converge on solutions verified against real physics.
• Designing the data, modeling, and compute backbone that lets all of the above scale and stay reproducible.
• Creating fast learned models that stand in for slow, expensive physical simulation.
• Building the agentic and tooling layer that lets these systems assist - and increasingly automate - expert engineering work.

You don't need to be an expert in all of it. You need world-class depth in something, the range to learn an adjacent domain fast, and the ambition to build something lasting. Backgrounds that aren't the "obvious" fit but map onto this problem: sparse linear algebra, model compression/quantization, graph theory and spectral methods, convex & non-convex optimization, evolutionary computation, information & coding theory, compilers and intermediate representations, distributed systems, scientific/physics-informed ML, and theoretical/mathematical work that you suspect "should" apply to structured physical domains. If you've ever looked at circuits - or any sparse, relational, physical system - and seen math others didn't, we want to talk.