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. Thats 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

• 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 dont use it

Environment

• In-person 5 days/week in San Francisco

• High-trust high-expectation culture

• Not a 95 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:

• 12 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 95 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:

• 35 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:

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