AI Hardware Design Engineer

TechDigital Group; Santa Clara (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

A leading technology company is seeking an Ai Hardware Design Engineer in Santa Clara, California. This role focuses on designing and optimizing AI-powered solutions for various applications. The successful candidate will develop surrogate models, implement workflows for performance specifications, and collaborate with experts to ensure compliance with scientific principles. A Master's or Ph.D. in a relevant field and proficiency in Python and ML frameworks are essential. Join us to innovate in the field of AI.

Qualifications

• Master's or Ph.D. in a relevant field.
• Strong proficiency in Python and ML frameworks.
• Experience with leading generative AI technologies.

Responsibilities

• Develop surrogate models and optimize generative AI workflows.
• Implement inverse-design workflows based on specifications.
• Collaborate to validate AI models with scientific laws.
• Create digital twins combining physics-based solvers with learned components for design-space exploration.
• Maintain containerized MLOps systems (Docker, Kubernetes) in HPC environments.
• Develop multi-objective optimization and uncertainty-quantification workflows for manufacturability and robustness.
• Collaborate with physicists and software engineers to ensure models comply with scientific laws.

Skills

Python

ML frameworks (PyTorch, TensorFlow)

MLOps

Generative AI (LLMs, diffusion models)

Computational materials methods (DFT, MD, phase-field modeling)

HPC environments

MLOps

Education

Master's or Ph.D. in Computer Science, Computational/Electrical Engineering, AI/ML, or related field

Tools

Docker

Kubernetes

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.