

Danta Technologies
Tech Lead (GenAI Design Engineer) | Santa Clara, CA, 95054
β - Featured Role | Apply direct with Data Freelance Hub
This role is a Tech Lead (GenAI Design Engineer) based in Santa Clara, CA, with a contract length of unspecified duration. It requires a Master's or Ph.D. in a relevant field, strong Python and ML framework skills, and experience in generative AI.
π - Country
United States
π± - Currency
β¬ EUR
-
π° - Day rate
Unknown
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ποΈ - Date
February 26, 2026
π - Duration
Unknown
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ποΈ - Location
On-site
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π - Contract
W2 Contractor
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π - Security
Unknown
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π - Location detailed
Santa Clara, CA
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π§ - Skills detailed
#TensorFlow #AI (Artificial Intelligence) #Automation #Cloud #PyTorch #ML (Machine Learning) #BI (Business Intelligence) #Python #Deployment
Role description
Role: Tech Lead
Location: Santa Clara, CA, 95054
Job Description
β’ 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.
β’ 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.
β’ 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.
β’ Build bi directional models that infer optimal process parameters for a given geometry and recommend geometry modifications when process latitude is insufficient.
β’ Create high fidelity digital twins combining physics based solvers (CFD, plasma, heat transfer) with learned surrogate components for rapid design space exploration.
β’ 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: MasterΓ’β¬ΒΏs or Ph.D. in Materials Science, Computational Engineering, AI/ML, or related field.
Technical Expertise
β’ Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
β’ Experience with generative AI (LLMs, diffusion models, graph based models).
β’ Knowledge of computational materials methods (DFT, MD, phase field modeling).
Additional Skills
β’ Familiarity with MLOps, HPC environments, and cloud deployment.
β’ Understanding of thermodynamics, crystallography, and mechanical properties of materials.
Notes:- All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Benefits: Danta offers a compensation package to all W2 employees that are competitive in the industry. It consists of competitive pay, the option to elect healthcare insurance (Dental, Medical, Vision), Major holidays and Paid sick leave as per state law.
The rate/ Salary range is dependent on numerous factors including Qualification, Experience and Location.
Role: Tech Lead
Location: Santa Clara, CA, 95054
Job Description
β’ 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.
β’ 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.
β’ 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.
β’ Build bi directional models that infer optimal process parameters for a given geometry and recommend geometry modifications when process latitude is insufficient.
β’ Create high fidelity digital twins combining physics based solvers (CFD, plasma, heat transfer) with learned surrogate components for rapid design space exploration.
β’ 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: MasterΓ’β¬ΒΏs or Ph.D. in Materials Science, Computational Engineering, AI/ML, or related field.
Technical Expertise
β’ Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
β’ Experience with generative AI (LLMs, diffusion models, graph based models).
β’ Knowledge of computational materials methods (DFT, MD, phase field modeling).
Additional Skills
β’ Familiarity with MLOps, HPC environments, and cloud deployment.
β’ Understanding of thermodynamics, crystallography, and mechanical properties of materials.
Notes:- All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Benefits: Danta offers a compensation package to all W2 employees that are competitive in the industry. It consists of competitive pay, the option to elect healthcare insurance (Dental, Medical, Vision), Major holidays and Paid sick leave as per state law.
The rate/ Salary range is dependent on numerous factors including Qualification, Experience and Location.






