AI Hardware Design Engineer

Position:AI Hardware Design Engineer

Location: Santa Clara CA (ONSITE ROLE)

Duration: 6 Months

Local candidates preferred.

Skills: Digital : PythonDigital : Machine Learning Experience Required: 6-8

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: Masters 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.