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.

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

Compensation: $200K–$300K + equity