Founding Full-Stack Product Engineer (AI-Native)

On-Site | San Francisco | Full-Time
$160K–$220K base + meaningful equity
H-1B transfers supported

The Bar

This is a founding engineer seat at an early-stage AI company building core infrastructure for LLM-powered systems.

If you need tight requirements, stable roadmaps, or a slow ramp, this is not the role.

If you ship fast, think clearly, and believe AI-native development changes how software should be built — keep reading.

What Youll Own

• End-to-end product engineering: backend, frontend, infrastructure

• AI-driven product features (context engineering, internal GenAI tooling)

• Telemetry, observability, and system intelligence

• Direct interaction with founders and early customers

• Fast iteration under incomplete information

You will regularly switch between products, systems, and customer realities. That's the job.

Non-Negotiables

• 5+ years building and shipping real products as a full-stack engineer

• Strong backend bias (60/40 backend/frontend)

• Production experience with TypeScript, Node, Next.js

• Startup experience where priorities changed weekly — and you still shipped

• You actively use AI tools in your daily workflow (not interested in learning)

• Clear, concise communicator — in writing and conversation

Strong Signals

• Seed to Series A startup experience

• Experience with developer tooling, telemetry, or infra-adjacent systems

• Public technical thinking (blog, GitHub, X, talks)

• Evidence of learning velocity over pedigree

Red Flags (We Will Screen Out)

• Long resumes with vague impact

• Pure big-tech backgrounds without recent startup experience

• Engineers are optimized for promotion cycles instead of ownership

• Low-intensity, low-urgency working styles

• Candidates who talk about AI but don't use it

Environment

• In-person, 5 days/week in San Francisco

• High-trust, high-expectation culture

• Not a 9–5 shop — consistency and output matter

• Small senior team, direct founder access

Interview Process

1. Founder screen (~15 minutes)

2. Final on-site loop (up to 4 hours)

Outcome

Best case: you help define a category and build systems that scale with AI.
Worst case: you leave with elite experience, a powerful network, and accelerated growth.

Reject Fast Checklist

Founding Full-Stack Product Engineer (AI-Native)
Internal Recruiter Use Only

1. Resume & Signal Quality (Immediate Filter)

Reject immediately if:

• Resume is over 2 pages with vague bullet points

• Impact is described as responsibilities instead of outcomes

• Heavy buzzwords, light specifics (“led initiatives”, “collaborated cross-functionally”)

• No clear evidence of shipping real products

• Resume reads like big-company internal tooling work

Green flags:

• 1–2 pages max

• Clear ownership + shipped outcomes

• Specific systems, decisions, and tradeoffs

• Concise, opinionated writing

2. Startup Reality Check

Reject if:

• Only experience is large public companies (FAANG / BigCo) without recent startup work

• Candidate expects stable roadmaps, long planning cycles, or heavy process

• Optimized for promotions, titles, or scope boundaries

️ Be cautious if:

• Startup experience is Series C+ only

• They were insulated from chaos or customer contact

Strong fit:

• Seed to Series A experience

• Has operated with changing priorities week-to-week

• Comfortable with ambiguity and incomplete specs

3. Full-Stack Depth (Backend Bias Required)

Reject if:

• Primarily frontend-only or design-heavy

• Backend experience is shallow or abstract

• Cannot clearly explain system design decisions

️ Deprioritize if:

• Full-stack in title only, backend work minimal

Strong fit:

• ~60/40 backend to frontend

• Comfortable owning APIs, data models, infra-adjacent logic

• Has made architectural tradeoffs under pressure

4. AI-Native Reality (Hard Gate)

Reject if:

• “Interested in AI” but not actively using it

• AI experience limited to a hackathon or side demo

• Cannot explain how AI improves their daily workflow

️ Deprioritize if:

• Uses AI occasionally but not as a force multiplier

Strong fit:

• Uses AI tools daily (coding, thinking, debugging, writing)

• Has built or shipped AI-powered features

• Clear belief that AI-native dev changes how software is built

Ask directly:

“How does AI change how you build software day-to-day?”

5. Communication & Thinking Clarity

Reject if:

• Rambling, vague, or buzzword-heavy explanations

• Struggles to articulate tradeoffs or reasoning

• Overly polished corporate speak

️ Be cautious if:

• Technically strong but unclear communicator

Strong fit:

• Clear, concise explanations

• Comfortable discussing mistakes and learnings

• Can simplify complex systems without dumbing them down

6. Intensity & Consistency Check

Reject if:

• Explicitly seeking strict 9–5 or low-intensity environments

• History of inconsistent availability or follow-through

• Missed interviews, slow responses, or flaky behavior

️ Deprioritize if:

• Energy level feels mismatched for an early-stage team

Strong fit:

• Consistent, reliable, high-agency operator

• Shows ownership mindset

• Comfortable with sustained intensity

7. Cultural Anti-Patterns

Reject if:

• Entitled tone or prestige-chasing

• Fixated on compensation before impact

• Blames past teams or managers excessively

• Overly rigid preferences on tools, process, or structure

Strong fit:

• Bias toward action

• Seeks learning over comfort

• Willingly pressure-tests assumptions against reality

8. Submission Standard (Non-Negotiable)

Do not submit if you cannot clearly answer:

• What did they personally own?

• What did they ship?

• Why them now for an early-stage AI company?

Every submission must include:

• 3–5 bullet recruiter summary

• Clear backend + AI signal

• Why this candidate survives chaos

Final Rule (Very Important)

If you would not personally take this person as a founding engineer on your own startup — do not submit them.

Quality > quantity.
Two strong submissions beat ten mediocre ones.

Client Name: Foam.ai

What to ask candidates•

1. Why are you so bullish on GenAI? 

2. How do you stay on top of new developments?

3. Can the candidate be on-site? If not, is the candidate willing to relocate?

4. What is their salary expectation?

5. How actively are they recruiting?