What are the responsibilities and job description for the AI Hardware Design Engineer Santa Clara - CA - California position at Jobs via Dice?
Job Description:
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 CVDALDetch 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, showerheadbaffle designs, and process conditions via generative or adjointtopology-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: Master s or Ph.D. in Computer Science, ComputationalElectrical Engineering, AIML, or related field.
Technical Expertise:
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 CVDALDetch 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, showerheadbaffle designs, and process conditions via generative or adjointtopology-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: Master s or Ph.D. in Computer Science, ComputationalElectrical Engineering, AIML, 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).
- Familiarity with MLOps, HPC environments, and cloud deployment.
- Proven experience (code repos, publications) bridging simulation software, hardware design, and ML.