AI Product Manager

K1X

. Own a rolling six-to-twelve-month AI roadmap for extraction coverage, accuracy, and the underlying platform; size it against team capacity, sequence dependencies, re-plan as evidence changes, and represent it in portfolio planning.

Posted 9/25/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in product management for AI/ML-powered solutions, with a strong focus on capacity planning, prioritization frameworks, and clear communication. Capable of translating technical metrics into user outcomes while managing stakeholder expectations and product requirements.

Highest-signal resume keywords

Product ManagementAI/ML Product OwnershipPrioritization FrameworksCapacity PlanningClear Communication

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Hard Skills

B2B Software DevelopmentDocument AIMachine LearningRoadmap SizingRelease ManagementPrecision and Recall Trade-offsEvaluation SetsCost Per InferenceDecision DocumentationProduct Requirements Documents

Soft Skills

Stakeholder ManagementCuriosityMeeting FacilitationUser-Centric ThinkingCollaboration

Industry Keywords

FintechRegtechVoice-of-Customer SynthesisRICEWSJFKanoOpportunity ScoringDefect-Intake Service LevelsTax-Year ReleaseClient Success

About the role

Key responsibilities & impact

Own a rolling six-to-twelve-month AI roadmap for extraction coverage, accuracy, and the underlying platform; size it against team capacity, sequence dependencies, re-plan as evidence changes, and represent it in portfolio planning.
Intake and prioritize requests for new document types, forms, and fields using defensible frameworks such as RICE, WSJF, Kano, opportunity scoring, and voice-of-customer synthesis.
Ensure prioritized items include definitions and test data needed for validation, and explain prioritization decisions to stakeholders.
Own how accuracy is defined and reported for executives, customers, Product, and Engineering.
Translate model-level measures such as per-field precision, coverage, and straight-through rate into user outcomes, including missed data, user corrections, and touches needed for filing-ready results.
Own the recurring accuracy report.
Provide product requirements for model and vendor decisions, including customer-relevant accuracy, cost, and latency thresholds, business cases, budgets, and release acceptance criteria.
Maintain decision records for model and vendor choices.
Plan engineering reserve around September–November filing peaks and own scope and dates for the January tax-year release.
Set defect-intake service levels with Client Success and QA, and keep proof-of-concept work time-boxed.
Partner with product managers and UX to turn user corrections into field-level provenance signals and a confidence-driven review experience.

Requirements

What you’ll need

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.