A well-funded startup building autonomous AI systems for chip design and verification
Machine Learning Engineer (ML for EDA)
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You'll spend mornings translating Verilog timing constraints into differentiable surrogate models, afternoons debugging why a graph neural net converges faster on place-and-route problems than human-tuned heuristics. Your PyTorch checkpoints directly influence how next-generation silicon gets taped out. The work sits at the collision of computational geometry, reinforcement learning, and the billion-dollar physics of fabrication.
What they're looking for
- 2+ years shipping ML models in PyTorch or TensorFlow, with at least one project touching physical systems or structured data
- Solid C/C++ for performance-critical inference paths and embedding into existing EDA workflows
- Familiarity with at least one domain among: VLSI design, electronic design automation, combinatorial optimization, or physics-based simulation
- Track record of reducing abstract research ideas to production code that runs on real hardware constraints
- Comfort working shoulder-to-shoulder with physical design engineers who speak in nanometers and picoseconds