Member of Technical Staff - Applied AI

Kindredventures; Palo Alto (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Meaningful equity stake

Fast-paced startup environment

Job summary

Kindredventures is seeking a Founding Member of the Technical Staff (Applied AI) to innovate at the intersection of chip design and AI. This role involves designing AI agents for core chip-design tasks and partnering with machine learning teams to optimize workflows.

The ideal candidate holds an MS or PhD in Electrical Engineering or a closely related field and has a solid background in RTL design. You will work in a fast-paced startup environment that challenges you with cutting-edge AI-driven chip design opportunities.

Qualifications

• Degree in Electrical Engineering, Computer Engineering or related field.
• Strong industry experience as an RTL design or Design Verification engineer.
• Excellent software engineering fundamentals.

Responsibilities

• Design and build AI agents for chip-design tasks.
• Own end-to-end agent workflows and tool usage.
• Partner closely with ML research teams.

Skills

Hardware Background

Software Engineering

Builder Mindset

Curiosity for AI

Education

MS or PhD in Electrical Engineering or related field

Tools

Python

Typescript

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