Founding Full-Stack Engineer

**Deepline** Founding Full-Stack Engineer; $180K–$220K base + meaningful equity (flex beyond for senior); New York City (On-site)

**About the Role** You'd be engineer #5–6 and own a business problem end-to-end like a founder — not push features against a spec. Two vectors the team is most excited about: building the "machine that builds the machine" (using agents and automation to triage the 15–20 customer feedback requests that arrive daily, design fixes, and minimize human-in-the-loop), and acting as GM of a hard business problem (e.g. enterprise pricing). The current opening is weighted toward the frontend (a full-stack hire is moving into a forward-deployed role, and this backfills him) — still full-stack, but spiky on the front end.

**Responsibilities** - Own a business problem end-to-end — form hypotheses, experiment, iterate, ship. - Build the "machine that builds the machine": agentic/automation systems that triage and act on 15–20 daily feedback requests. - Write and ship production code daily, with strong frontend craft; manage coding agents and the codebase to deliver outcomes. - Turn raw customer feedback into product direction; help shape a fast-moving 2–3 month roadmap. - Extend the Context API and integrations across the 40+ GTM data providers.

**Required Skills** A. 3+ years building production systems, with clear velocity — promotions or fast-growing scope (e.g. 3→10+); senior, hands-on engineers strongly preferred. B. Strong full-stack engineering across Python (primary), TypeScript/JavaScript, and SQL — spiky toward the frontend for this seat (excellent React/TypeScript and product-UI craft). C. Heavy hands-on use of Claude Code / coding agents (near-prerequisite) — a real feel for where agents are strong and where they break. D. A "data mindset" — fluency reasoning about data access, modeling, and pipelines.

**Bonus Skills** - Data engineering or data science background. - Former founder or early-stage startup experience. - Familiarity with GTM tooling (CRM, enrichment, sequencing) or enterprise data systems.

**Logistical Info** - Location: NYC, fully in-person (5 days/week). Intense "marathons and sprints" culture with some weekend work — explicitly not a mandated 996. - Compensation: $180K–$220K base + meaningful equity; flexibility to go beyond for more senior candidates.

**Ideal Background** - Key filter is one strong "spiky" signal: elite school pedigree, a standout company logo, a side project with quantitative traction (GitHub stars, revenue), or a past startup/founder stint. Without a spike, hit-rate drops. - Strongest signal — intense, high-bar engineering cultures; Meta candidates have done well. - For this seat, frontend-spiky full-stack engineers (strong React/TypeScript, product-UI sensibility). - Big tech + 1–2 years at a startup is a great combination; data eng / data science backgrounds do well. - Velocity over tenure: promotions and growing scope; a former founder is an especially strong sign.

**Green Flags** - Former founder; high agency; reasons from first principles. - Already a heavy Claude Code / agent user. - Excited by traction and wants to "double down"; brings genuine intensity. - Intellectual curiosity (e.g. pursuing serious learning alongside work). - Wants to own a full business problem, not just close tickets.

- Trading/DeFi-protocol domain exposure (Brian, Aug 24: "Talos is a good parallel") — trading-experienced candidates strongly preferred over pure infra engineers. **Red Flags** - Slow-moving, "outside-looking-in" cultures — Salesforce and LinkedIn called out specifically. - 4–5 years at one company with no promotion or growth (low velocity). - No startup exposure / only comfortable in structured corporate settings (straight from big tech like Capital One is higher-risk). - Unwilling to work hard, including the occasional weekend.

**Interview Process** (fast by design; in-person in NYC) 1. Intro/screen (15–30 min) with Chirag — first sell and initial fit. 2. Technical interviews including system design (being updated from a generic "redesign DoorDash" prompt to a Deepline-specific problem). 3. Work trial / on-site in NYC. 4. Verbal offer delivered 1:1. Feedback within ~24h between stages.