AI Product Engineer
Product Lead Engineer (AI Systems / 0->1)New York | In-Person (4–5 days/week)$150K–$300K + EquityThe OpportunityWe’re working with a venture-backed team building AI-native infrastructure for one of the most complex, high-friction systems in the U.S.: healthcare operations.This is not incremental optimization.They’re rebuilding core workflows — claims, billing, clinical documentation — as end-to-end automated systems powered by LLMs, structured data pipelines, and human-in-the-loop feedback loops.The problems are messy:inconsistent inputsadversarial edge cases (payer rules, denials, compliance)real-world financial impactThe upside is equally large:direct control over revenue flowability to reshape cost structures in a $100B+ marketsystems that compound in performance over timeThe team includes engineers from top AI, quant, and systems backgrounds, and they’re scaling aggressively post-Series A.The RoleThey’re hiring a Product Lead Engineer to own a full problem space — not a feature, not a service — a domain.This is a hybrid role by design:Product ownership (defining what to build and why)Systems thinking (designing how it works end-to-end)Execution (shipping and iterating quickly)You’ll take a workflow like:clinician input -> structured data -> payer interaction -> cash collection…and turn it into a reliable, automated system with measurable performance.What You’ll Actually DoOwn a Domain End-to-EndDefine system boundaries, inputs/outputs, and failure modesTranslate ambiguous workflows into structured, testable systemsShip iteratively with tight feedback loopsDesign AI + Systems TogetherDecide when to use models vs rules vs hybrid approachesBuild feedback loops (human-in-the-loop, retraining signals, error correction)Optimize for accuracy, latency, and economic impact — not just functionalityOperate on Real MetricsInstrument systems around:claim success ratesdenial reductiontime-to-paymentDebug edge cases and continuously improve system performanceWhat They’re Looking ForCore ProfileStrong technical background (CS, math, physics, or similar)Experience building 0->1 systems with real-world constraintsComfortable reasoning about:distributed systemsdata pipelinesmodel behavior + failure modesTech StackInfra/Ops: AWS, Terraform, Docker, Datadog, Sentry, Modal, Frontend: Next JS, React, Posthog, Amplify AWS, Backend: FastAPI, Pydantic, PostgreSQLHigh-Rigor SignalsBackground in quant, top-tier engineering teams, or highly technical startupsEvidence of fast learning and high slope (rapid progression, outsized ownership)Ability to break down ambiguous problems into structured solutionsMindsetYou optimize for correctness and outcomes, not just shipping fastYou’re comfortable operating without clear specsYou care about building systems that improve over timeWhy This RoleReal technical depth — not CRUD apps, but complex, high-stakes systemsFull ownership — you define and build, not just executeTight feedback loops — your work directly impacts revenue + operationsAI-native from first principles — not retrofitting legacy systemsClear trajectory -> Director-level ownership of a domainWho This Resonates WithEx-quants or engineers who want to apply rigor to real-world systemsBuilders who’ve done 0->1 and want more ownershipPeople who enjoy messy, adversarial problem spacesEngineers who think in systems, not endpoints