Member of Technical Staff - Applied AI

About the role

Architect is a frontier AI lab focused on designing custom Application-Specific Integrated Circuits (ASICs) at scale. Our mission is to collaborate with evolving machine learning (ML) workloads to develop domain-specific chips that surpass current hardware limitations.

What You'll Do

• Design and build AI agents capable of tackling core chip-design tasks, aligning model behavior with real-world hardware engineering practices.
• Own the entire workflow for these agents, including scaffolding, tool usage, evaluation frameworks, and the specialized infrastructure necessary for practical application.
• Ensure the hardware integrity of the AI models by curating high-quality data, setting clear evaluation criteria, and incorporating engineering judgment into the model's output.
• Collaborate closely with the ML research, post-training, and infrastructure teams to transform hardware domain knowledge into actionable metrics, benchmarks, and training data.
• Move swiftly in a pioneering environment, prototyping, testing, refining, and shipping new capabilities that address complex chip-design challenges.

Qualifications & Skills

• Education: A Master’s or PhD in Electrical Engineering, Computer Engineering, EECS, or a related field.
• Hardware Background: Significant industry or research experience as an RTL designer or verification engineer, with a thorough understanding of the entire chip design process.
• Software Engineering: Proficiency in writing clean, robust Python or TypeScript code, building tools, and operating in modern engineering environments.
• Builder Mindset: Proven ability to take on ambiguous projects from start to finish, rapid prototyping, and successfully implementing solutions.
• Curiosity for AI: Enthusiasm for applying cutting-edge AI techniques to hardware design, without requiring prior experience in applied AI or ML research.
• Bonus: Prior experience in AI for chip design or AI4EDA initiatives at major tech firms like Google, NVIDIA, or chip/EDA companies.
• Publications or Open-Source Contributions: Experience contributing to academic conferences such as DAC, ICCAD, DVCon, MLCAD, NeurIPS, ICLR, ICML, or similar venues.
• Early Startup Experience: Background as an early employee at a deeptech or AI startup.

What We Offer

• Competitive compensation package including equity stakes.
• A dynamic, fast-paced startup environment with significant autonomy and impactful work.
• Challenging and cutting-edge AI-driven chip design projects.