Member of Technical Staff - Applied AI
Job Description
About Architect
Architect is a frontier AI lab for chip design. We build AI models and tools for on-demand custom ASICs at scale. Our goal is to co-design custom ASICs alongside evolving ML workloads, and enable a new era of domain-specific chips that unlock capabilities impossible with current hardware paradigms. Born out of Stanford Research, our team blends AI with Silicon with a founding team from Anthropic, Google DeepMind, Meta SuperIntelligence, xAI, Apple and Intel.
What You'll Do
As a Founding Member of the Technical Staff (Applied AI) at Architect, you'll sit at the intersection of chip design and frontier AI — translating deep hardware engineering expertise into agentic systems that can reason about, generate, and verify real silicon.
Design and build AI agents that tackle core chip-design tasks, grounding model behavior in how real hardware engineers actually work.
Own end-to-end agent workflows: scaffolding, tool use, evaluation harnesses, and the domain-specific infrastructure that makes agents useful on actual design problems.
Serve as the hardware conscience of the model — curating high-quality data, defining evaluation criteria, and encoding the engineering judgment that separates plausible outputs from correct ones.
Partner closely with the ML research, post-training, and infra teams to turn hardware domain expertise into reward signals, benchmarks, and training signal.
Move fast in a 0→1 environment: prototype, dogfood, break things, iterate. Translate ambiguous chip-design challenges into concrete agent capabilities that ship.
What We'd Like to See
Qualifications & Skills:
Degree: MS or PhD in Electrical Engineering, Computer Engineering, EECS, or a closely related field.
Hardware Background: Strong industry or research experience as an RTL design or Design Verification engineer, with a solid understanding of the modern chip design flow end to end.
Software Engineering: Excellent software engineering fundamentals — comfortable writing clean, production-grade Python or typescript, building tooling, and working in modern engineering environments. This is a non-negotiable bar.
Builder Mindset: Demonstrated ability to own ambiguous problems end to end, prototype quickly, and productionize what works. Pragmatic, not precious.
Curiosity for AI: Genuine excitement about applying frontier AI to hardware. No prior applied-AI or ML research background is required — we'll meet you where you are.
Bonus:
Prior experience on AI-for-chip-design or AI4EDA efforts at Google, NVIDIA, or at chip / EDA companies.
Experience building, using, or evaluating LLM-based tooling for engineering workflows.
Publications or open-source contributions at the intersection of ML and EDA (DAC, ICCAD, DVCon, MLCAD, NeurIPS, ICLR, ICML).
Experience as an early engineer at a deeptech or AI startup.
What We Offer
Competitive salary and meaningful equity stake
Fast-paced startup with autonomy and visible impact
Cutting-edge AI-driven chip design challenges