AI Hardware Design Engineer

Job Title: AI Hardware Design Engineer

Location: Santa Clara CA - Onsite

Duration: 6 Months

***NOTE: Experience for SciML R&D and exposure in Neural operators, PINNs etc. is required***

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.

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.

·         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.