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About This Role
K1x turns tax documents (K-1s, K-3s, state schedules, 990s, and a growing range of other filings and
statements) into filing-grade structured data for CPA firms, family offices, and institutional investors.
Accuracy and rework are the first thing our buyers ask about, and the models, prompts, and rules that
produce each extracted value are the product surface that answers them. That surface needs a
product manager fluent in how ML systems behave: how they are evaluated, where they fail, what
they cost, and when to replace a component rather than tune it. This role is the product manager for
the AI team: how its models and services are supplied to every K1x product line, partnering with the
Head of AI on delivery and with the other product managers on where those capabilities land.
What You'll Do
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.
Requirements
- 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.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At k1x, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
k1x AI Hiring
k1x has 1 open AI role right now. They're hiring across AI Product Manager. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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