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

Work arrangement
No