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GenDesign/Inverse Design Ai Engineer
GenDesign/Inverse Design Ai EngineerInherent Technologies • Santa Clara, CA, United States
GenDesign / Inverse Design Ai Engineer

GenDesign / Inverse Design Ai Engineer

Inherent Technologies • Santa Clara, CA, United States
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Position : GenDesign / Inverse Design Ai Engineer

Location : Santa Clara, CA

  • Day 1 Onsite

Duration : 1 Years

Mandatory Areas

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, 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.
  • Understanding of thermodynamics, crystallography, and mechanical properties of materials.
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