Member of Technical Staff, Applied AI

Company: GC AI
Role: Member of Technical Staff, Applied AI
Compensation: $180,000 – $430,000 base + equity
Location: Remote (US & Canada); in-office Tuesday–Thursday required if within ~50 miles of San Mateo, CA or Provo, UT hubs
Company Overview
GC AI is the fastest-growing legal AI platform built for in-house legal teams, enabling counsel to draft, review, and analyze contracts and legal documents in seconds. Used by more than 1,800 companies—including 150+ public companies and 25+ unicorns like News Corp, Miro, Snyk, Skims, Vercel, Zscaler, and TIME—GC AI maintains an NPS of 70–75 and recently secured a #1 SaaS customer satisfaction award. GC AI raised a $60M Series B in November 2025 at a $555M valuation led by Scale Venture Partners and Northzone. CEO Cecilia Ziniti previously led legal initiatives at Amazon Alexa, Cruise, and Replit. Roughly 20% of the team are attorneys who submit pull requests, and the engineering org is under 20 people and doubling.
Role Summary & Day-to-Day Responsibilities
This is a full-stack engineering seat with a heavy AI execution focus. You will own features from concept to production, working on LLM orchestration, retrieval-augmented generation (RAG), and model evaluation:
Architect and ship LLM orchestration and RAG pipelines directly into production.
Run evaluations and rigorous testing frameworks across 4–5 task-tuned production models whenever updates drop.
Integrate third-party AI capabilities, emerging developer tools, and external connectors into the platform.
Build robust, repeatable engineering frameworks to prototype and test new foundation models rapidly.
Review and ship pull requests contributed by GC AI’s R&D attorneys.
Mentor team members as the Applied AI engineering pod scales to 5.
Required Qualifications & Screening Criteria
Deep, production-grade hands-on experience in TypeScript/JavaScript and React.
Demonstrated background shipping AI products, chatbots, or LLM integrations into production.
Strong track record across full-stack, frontend, or backend at high-caliber product companies with early-stage startup ownership.
A pragmatic shipping mindset rather than pure academic or research ML/data science.
Startup experience
Stable career tenure without a recurring pattern of short stints.
Nice to have: RAG architecture depth, multi-agent frameworks, legal tech domain experience, or founding engineer experience with an exit.

Work arrangement
Yes