Develops and scales MPI+CUDA PDE solvers and neural operators for electromagnetic simulations on GPU clusters for IC design. Requires PhD in computational physics/math, expertise in numerical methods, C++/CUDA, HPC, and neural operators for physics problems.
Trains frontier LLMs on semiconductor design/verification data (RTL, netlists, PDKs) for automated chip development. Develops synthetic data generation, model distillation, evals, and scales training across thousands of GPUs.
Post-trains frontier AI models using reinforcement learning to autonomously handle semiconductor design tasks like chip architecture optimization, RTL code generation, simulations, and verification. Collaborates with hardware experts to build RL environments, reward functions, and evaluation frameworks.
Develops and scales MPI+CUDA PDE solvers and neural operators for electromagnetic simulations on GPU clusters for IC design. Requires PhD in computational physics/math, expertise in numerical methods, C++/CUDA, HPC, and neural operators for physics problems.
Trains frontier LLMs on semiconductor design/verification data (RTL, netlists, PDKs) for automated chip development. Develops synthetic data generation, model distillation, evals, and scales training across thousands of GPUs.
Post-trains frontier AI models using reinforcement learning to autonomously handle semiconductor design tasks like chip architecture optimization, RTL code generation, simulations, and verification. Collaborates with hardware experts to build RL environments, reward functions, and evaluation frameworks.