Ai Hardware Design Engineer

Please find the detailed JD for the open position in our team at Santa Clara, CA. Title: Ai Hardware Design Engineer Location: Santa Clara, CA We are seeking an Ai Hardware Design Engineer to join our team and drive innovation in AI-powered solutions.

This role involves designing, developing, and optimizing generative AI models and workflows for applications such as content creation, product design, and intelligent automation. Develop forward surrogate models for CVD/ALD/etch chambers mapping geometry, gas chemistry, flow, temperature, and power to film-uniformity, step-coverage, particle behaviour, and thermal outcomes. Implement inverse-design workflows where target performance specifications generate feasible chamber geometries, showerhead/baffle designs, and process conditions via generative or adjoint/topology-optimization methods. Build bi-directional models that infer optimal process parameters for a given geometry and recommend geometry modifications when process latitude is insufficient. Create high-fidelity digital twins combining physics-based solvers (CFD, plasma, heat transfer) with learned surrogate components for rapid design-space exploration. Platform & MLOps Infrastructure: Implement and maintain robust, containerized MLOps systems (Docker, Kubernetes) in HPC environments to deploy models efficiently. Develop robust multi-objective optimization and uncertainty-quantification workflows to ensure AI-generated designs are manufacturable, robust to variation, and compatible with downstream yield requirements. Collaborate with physicists, domain experts, and software engineers to validate that AI models comply with fundamental scientific laws. Required Skills & Qualifications Education: Master's or Ph.D. in Computer Science, Computational/Electrical Engineering, AI/ML, or related field.

Technical Expertise: Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow). Experience with generative AI (LLMs, diffusion models, graph-based models). Knowledge of computational materials methods (DFT, MD, phase-field modeling). Additional Skills: Familiarity with MLOps, HPC environments, and cloud deployment. Proven experience (code repos, publications) bridging simulation software, hardware design, and ML.