AI Hardware Design Engineer
Pentangle TechSanta Clara, CA
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Job Description
Job Title: AI Hardware Design Engineer
Location: Santa Clara, CA
Duration: Long Term
Client: AMAT
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 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.
- 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, Computational/Electrical 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.
- Proven experience (code repos, publications) bridging simulation software, hardware design, and ML.
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