GenDesign / Inverse Design Ai Engineer ::Santa Clara, CA (5Days onsite).

Role: GenDesign / Inverse Design Ai Engineer
Location: Santa Clara, CA

Must Have Skills
Skill 1 Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
Skill 2 Experience with generative AI (LLMs, diffusion models, graph-based models).
Skill 3 We are seeking a Generative AI (GenAI) Design Engineer to join our team and drive innovation in AI-powered solutions

Good To have Skills
Skill 1 Familiarity with MLOps, HPC environments, and cloud deployment

We are seeking a Generative AI (GenAI) 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.
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.

Required Skills & Qualifications
Education: Master's or Ph.D. in Materials Science, Computational Engineering, AI/ML,

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 modelling).

Additional Skills:
Familiarity with MLOps, HPC environments, and cloud deployment.
Understanding of thermodynamics, crystallography, and mechanical properties of materials.

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