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: Masters 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.
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 ...
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: Masters 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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