AI Hardware Design Engineer / Applied Scientist – Semiconductor AI (CVD/ALD, Digital Twin)

Job Description: • 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 behavior, 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: o Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow). o Experience with generative AI (LLMs, diffusion models, graph-based models). o Knowledge of computational materials methods (DFT, MD, phase-field modeling).