Senior Silicon ML Engineer

About the role

The Silicon ML Engineer (II/III) develops AI-driven capabilities for silicon design and verification workflows by infusing hardware domain knowledge into modern AI and large-model systems. This role requires close integration between machine learning methods and semiconductor design processes, data, and constraints. The engineer will build domain-aware ML/LLM and agent-based systems and translate research prototypes into production-grade tooling used in silicon design flows. Level will be determined based on experience, technical depth, and scope of ownership.

Responsibilities

• Design and implement AI-enabled silicon design and verification workflows with explicit incorporation of hardware domain knowledge and constraints
• Encode domain structure, rules, and expert feedback into ML/DL model pipelines, prompts, evaluation, and system logic
• Prepare and curate silicon design and verification datasets with domain-aware labeling and quality controls
• Build AI for hardware research prototypes and translate them into scalable, production-quality systems integrated with design flows
• Collaborate directly with other chip design and verification engineers to ensure domain correctness and practical usability
• Contribute to system architecture and integration with internal silicon design tools and workflows in LLM-based agentic systems
• Produce technical documentation and communicate design decisions and tradeoffs to cross-functional stakeholders

Requirements

• PhD in Computer Science, Electrical Engineering, or a relevant field with 3+ months of applied research or industry experience
• Strong programming skills in Python (PyTorch or similar ML frameworks preferred)
• Hands-on experience with machine learning / deep learning projects
• Experience working with messy, real-world data and defining quality metrics
• Demonstrated ability to work across abstraction layers—from research prototypes to production systems
• Demonstrated exposure to hardware design, verification, or EDA workflows through coursework, research, or industry work
• Proven track record of collaboration with other deeply technical teams