Head of AI

Responsibilities

• Set AI Technical Vision: Define the architecture and roadmap for our AI systems. You will make the critical decisions on model architecture, training strategy, and system design that determine our technical trajectory
• Build auto-pilot chip design systems: Lead development of LLMs that generate production-quality RTL. You will personally architect the systems that transform specifications into silicon-ready designs
• Develop optimization and design space exploration loop: Own the feedback system where verification results improve model efficacy. You will design the architecture that enables our AI to learn all the designs that are generated, fixed and optimized
• Lead Document Understanding: Build systems that parse architecture specs, design documents, and protocol definitions into structured representations our models can use
• Hire and Develop the Team: Build the AI engineering team to a large team over the next three years. You will recruit top talent, set technical standards, and mentor engineers
• Partner with Chip Design: Work closely with our chip design and verification engineers to ensure AI-generated designs meet production quality standards. You will bridge the gap between AI and hardware
• Drive Research Agenda: Identify and pursue research directions that advance our capabilities. You will keep us at the frontier of AI for hardware design

Qualifications

• AI/ML Leadership: 8+ years of experience in ML/AI, with years of experience in leading teams. Shipped production AI systems that handle real-world complexity
• LLM Expertise: Deep hands-on experience with large language models—dataset creation, training, fine-tuning, and deployment. Understand transformer architectures, scaling laws, and the practical challenges of production LLM systems
• AI Agents and Code Generation: Worked on AI agents and code generation, program synthesis, or compiler systems
• Hands-on experience building AI agent systems and apps, with proficiency in Python and PyTorch
• Team Building: Hired and developed AI engineers. You can identify exceptional talent and create an environment where they do their best work
• Systems Thinking: Design systems that integrate models with infrastructure, data pipelines, and user workflows