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Founding Engineer (AI) Union Square NYC FTE Comp range of $200K – $400K plus up to 2% equity Why candidates should join Bootstrapped and highly profitable - went from $0 to $80M in six months with a tiny cost basis. No VC dependency or dilution risk. You'd be the first engineering hire. Every improvement you ship hits the bottom line, often within hours. Comp range of $200K – $400K plus up to 2% equity in a cash-flow-positive business that's already ripping. AI-native from day one - agentic workflows power real-time pricing and bidding systems in live markets. You're building classical ML systems using AI developer workflows. Work experience History of rapid career progression. Hard skills Strong data intuition and curiosity about the business: reasons from data to a pricing or product call. Deeply AI forward in their developer workflows. Seniority 3 - 10 years of experience post- grad software engineering with deep adoption of agentic AI workflows. Work experience Experience at top- tier technology companies including smaller companies that have been successful or are solving really hard problems as well as larger companies with strong engineering orgs. Experience in platform engineering. Hard skills Experience designing and operating data- intensive production systems, ideally Python- based. Soft skills Comfortable taking risks and being wrong. Strong opinions with demonstrated ability to push back and challenge decisions. Traits to avoid Mainly quant, ML research or forward- deployed background rather than backend/systems engineering Extended breaks in employment history.
JD: We are looking for a Founding Engineer with 3 - 10 years of experience to join a highly profitable, bootstrapped trading platform that has scaled from $0 to $80M in six months. You'll be the first engineering hire building the world's most advanced pricing technology in live event ticketing where every improvement you ship hits the bottom line within hours.
What you will do:
  • Build and own core platform systems - the pricing engine, bidding system, and data infrastructure that power real-time ticket trading
  • Design AI research pipelines that turn today's interactive, session-by-session model research into continuously running infrastructure
  • Build A/B testing frameworks for the pricing engine and optimize bidding system throughput
  • Create AI-powered operational systems for automated triage, production failure response, and model drift detection
  • Tech Stack
  • - Python, AI/ML, Agentic AI Workflows, Data Pipelines, Real time systems, Classical Machine Learning

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