Founding Engineer (Stackpoint Portfolio Company)

Stackpoint is a venture studio that builds and funds vertical AI companies transforming legacy industries. The Founding Engineer will join an early-stage company to build and scale AI-native products, workflows, integrations, and human-facing tools from initial customers to broader adoption. The role owns engineering end to end, including agentic AI, infrastructure, deployment, monitoring, data handling, and production reliability.

Responsibilities:

• Build new workflows and products from scratch
• Take new workflows, services, and customer segments from idea to production, working directly with product, operations, and customers
• Make the "build to learn" versus "build to scale" call explicitly, and throw away a prototype when the team learns something important
• Scale what's working
• Extend a working platform built with the studio team, and take it from first customers to many
• Find the bottlenecks before customers do: performance, reliability, cost, and failure modes
• Build foundations so onboarding the next customer or market is configuration, not a project
• Ship, deploy, and monitor what you build, including CI/CD, logging, and alerting
• Maintain and extend the internal tools and agents the team relies on to operate
• Ship agentic AI workflows that hold up in production
• Build extraction, classification, and retrieval over messy, unstructured industry data: documents, forms, scans, and records
• Orchestrate multi-step agents with tool use, structured outputs, and deterministic guardrails
• Set the confidence thresholds that decide what goes to a human reviewer
• Make model selection and cost/latency tradeoffs with data
• Integrate with systems that weren't built to be integrated with
• Connect to the legacy software, web portals, and email and file-based workflows that high-barrier industries still run on, including agent-driven browser automation where there's no API
• Build reusable integration patterns where volume justifies it, and optimize the paths that carry the most volume
• Build the human-facing product
• The internal operations console: the human-in-the-loop surface where operations and servicing staff review, correct, and approve AI output quickly. Their corrections feed back into evaluation
• The client-facing B2B experience: how customers submit work, track status, and act on results
• Handle PII and sensitive financial data correctly across both, with access control, audit trails, and tenant isolation