Full-Stack Engineer

Description:

Target Profile:

Our client wants a high-caliber full-stack/product engineer who also understands LLMs and agents.

The key distinction from their Research Engineer search:

FULL-STACK ENGINEERING FIRST → AGENT DEPTH SECOND

Candidates do not need to be hardcore agent researchers. The client explicitly allows engineers with hands-on LLM/agent experience or demonstrable ability to ramp quickly.

Core profile:

FULL-STACK → END-TO-END OWNERSHIP → AGENTS/LLMs → CUSTOMER-FACING → 0→1 → HIGH AGENCY

What They’ll Actually Build:

This role owns the product that helps engineers understand why production agents are failing and whether fixes actually work.

Ideal Candidate:

Someone who can:

TALK TO CUSTOMER → IDENTIFY PROBLEM → DESIGN PRODUCT → BUILD BACKEND → BUILD UI → SHIP → OBSERVE → ITERATE

They shouldn't need:

CUSTOMER → PM → PRD → ENGINEER

They should be comfortable collapsing that chain themselves.

Requirements

Must Haves:

- 3–7 years full-stack engineering
- Strong production engineering
- Owns systems data layer → backend → UI
- Hands-on LLM/agent experience or compelling evidence of ability to ramp rapidly
- Strong technical problem solving
- Comfortable with ambiguity
- Excellent communication
- Direct customer interaction
- Product instincts
- High agency
- Can define what to build rather than waiting for specs
- SF / willing to relocate
- 5 days/week in office

About 30% of the role is customer-facing, which is unusually important for this search.

Strong Green Flags:

- Palantir
- Databricks
- Datadog
- Cognition
- Decagon
- Sierra
- Linear
- Cursor
- Ramp
- Figma
- Vercel
- CockroachDB
- Retool
- Modal
- Anyscale
- Runway
- Applied Intuition
- Anduril
- Notion
- Nomic
- MotherDuck
- Strong product company + technical depth
- FDE / Solutions Engineer with serious coding depth
- Early startup engineer
- Ex-founder
- Founder-to-be profile
- Coding competitions
- Research + production engineering
- AI evals
- Observability
- Agent behavior monitoring

Nice-to-Haves:

- Agent evals
- Observability
- Agent behavior monitoring
- FDE experience
- Solutions engineering
- Founder background
- Early startup
- Strong infrastructure
- Backend depth
- AI product experience
- Coding competitions
- Research experience

Red Flags:

- Agent experience but mediocre engineering
- AI wrapper/demo experience
- Research without production ownership
- Pure backend/infrastructure without product range
- Pure frontend without systems depth
- Never owned data → UI
- No customer-facing experience
- Needs specifications handed down
- Weak communicator
- Treats FDE/product engineering as a fallback
- Slow-moving candidate / weak commitment
- Cannot work in SF

Benefits

Compensation: $200K–$300K + equity