AI Product Manager

Summary

K1X turns tax documents into filing-grade structured data for CPA firms, family offices, and institutional investors. The AI Product Manager will lead the AI product roadmap, prioritize document and extraction needs, define accuracy metrics, guide model and vendor decisions, manage release capacity around the tax calendar, and improve the user correction loop.

Responsibilities

• The AI roadmap. A rolling six-to-twelve-month roadmap for extraction coverage, accuracy, and the platform underneath them, sized against real team capacity, sequenced with dependencies made explicit, and re-planned when the evidence changes. You represent it in portfolio planning alongside the other product roadmaps
• The demand queue. Intake and prioritization of new document types, forms, and fields requested by Product, Tax Content, Sales, and Client Success. You rank the queue with a defensible framework (RICE, WSJF, Kano, opportunity scoring, voice-of-customer synthesis), make sure each item arrives with the definition and test data needed to validate it, and explain the ranking to those who did not get their item first
• Accuracy as a product metric. Own how accuracy is defined and reported for each audience: executive, customer, product, engineering. Translate model-level measures (per-field precision, coverage, straight-through rate) into what a user experiences: what we missed, what they had to touch, how many touches it took to reach a filing-ready result. Own the recurring accuracy report
• Product requirements for model and vendor decisions. Engineering evaluates and recommends what serves each stage of the pipeline: a frontier model, one of our own models, or a vendor service. You supply the product side of that decision: which accuracy, cost, and latency thresholds actually matter to customers, the business case and budget for a change, and the acceptance criteria a release must clear. You keep the decision record so the reasoning survives
• Capacity and the tax calendar. Filing peaks in September, October, and November drive usage; a tax-year release lands every January. You plan engineering reserve around the peaks, own scope and dates for the tax-year release, set the defect-intake service level with Client Success and QA, and keep proof-of-concept work time-boxed
• The correction loop. Users correct extraction output inside our products. You partner with those product managers and UX so corrections become a usable signal with field-level provenance, and over time a confidence-driven review experience

Skills

• 4+ years of product management on shipped B2B software, ideally fintech or regtech where a wrong number costs more than a slow one, with at least 2 years owning an AI/ML-powered product surface: document AI or extraction, search and ranking, LLM features, or an ML platform
• Fluent in the mechanics: capacity planning against a real team, prioritization frameworks (RICE, WSJF, Kano, voice-of-customer synthesis), roadmap sizing, release management, and PRDs an engineer would actually read
• Understand how ML products fail differently from software: precision and recall trade-offs, evaluation sets, drift, cost per inference, and why "the model got it wrong" is a product question first
• Have made or shaped build, buy, or replace calls on model or vendor components, and can walk through one that went badly
• Write clearly and run a tight meeting. Half this job is turning engineers' conviction into a decision document Product, Tax, and Finance can act on
• Tax-domain knowledge is not required; Tax Content owns the rules and CPAs adjudicate. Curiosity is; the interesting failure modes live in the footnotes

Qualifications

Must Haves

• 4+ years of product management on shipped B2B software, ideally fintech or regtech where a wrong number costs more than a slow one, with at least 2 years owning an AI/ML-powered product surface: document AI or extraction, search and ranking, LLM features, or an ML platform
• Fluent in the mechanics: capacity planning against a real team, prioritization frameworks (RICE, WSJF, Kano, voice-of-customer synthesis), roadmap sizing, release management, and PRDs an engineer would actually read
• Understand how ML products fail differently from software: precision and recall trade-offs, evaluation sets, drift, cost per inference, and why "the model got it wrong" is a product question first
• Have made or shaped build, buy, or replace calls on model or vendor components, and can walk through one that went badly
• Write clearly and run a tight meeting. Half this job is turning engineers' conviction into a decision document Product, Tax, and Finance can act on
• Tax-domain knowledge is not required; Tax Content owns the rules and CPAs adjudicate. Curiosity is; the interesting failure modes live in the footnotes

Benefits

• Remote work