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About This Role
Workiva's Growth organization is building the next generation of AI\-native products. We are seeking a Senior Product Manager, to transform ambiguous, complex customer challenges into validated, high\-trust enterprise product solutions. In this role, you will champion early product spaces from inception to evaluation: uncovering systemic customer challenges, developing rapid technical prototypes, determining where artificial intelligence drives measurable value, and validating product\-market fit with clear evidence before scaling.
This is a technical, zero\-to\-one validation role focused on systemic exploration rather than static roadmap maintenance. Your core challenge will be architecting probabilistic AI experiences that deliver high utility while maintaining strict predictability, data security, and user safety within a highly regulated, high\-trust enterprise compliance environment.
What You’ll Do
- *Deep Customer Discovery \& System Mapping:* Lead qualitative user interviews, observational workflow walk\-throughs, and technical discovery sessions to analyze how enterprise finance, compliance, and audit teams manage complex operational data. Diagram enterprise dependency structures, human\-to\-system handoffs, multi\-stakeholder approval gates, and historical operational failure points
- *Hypothesis Formulation \& Strategic Betting:* Synthesize messy, fragmented user signals into structured, reusable problem definitions rather than one\-off custom feature requests. Translate field observations into precise product\-market hypotheses, defining the explicit customer value, repeatability, and expected business impact of your proposed AI workflow interventions
- *Rapid Prototyping \& Technical Validation:* Collaborate closely with design and engineering partners to develop, script, and launch low\- and high\-fidelity validation concepts (utilizing tools like SQL, Python, Retool, or Streamlit). Establish clear Minimum Viable Product (MVP) boundaries, explicit non\-goals, and concrete validation timelines to decisively recommend whether to scale, pivot, or sunset an early\-stage product vector
- *Agentic Experience \& Trust Frameworks:* Translate complex enterprise compliance anxieties into intuitive, high\-trust agentic experiences. You will define the interaction paradigms that dictate how autonomous agents collaborate with humans—ensuring the product interface clearly signals when an AI is making a probabilistic recommendation versus when deterministic rules apply. You will champion the design of elegant human\-in\-the\-loop touchpoints that allow users to easily audit, verify, and approve AI\-driven outcomes without sacrificing workflow momentum
- *Agentic Boundary \& Security Governance:* Formulate, model, and implement digital authority boundaries for autonomous enterprise AI agents. Design enterprise\-grade, permission\-aware access policies that precisely define what an agent can read, summarize, compile, update, escalate, or execute, ensuring absolute auditability under active corporate compliance guidelines
- *Cross\-Functional Alignment \& Performance Analysis:* Act as the central, transparent product leader connecting technical engineering workflows, UX design iterations, data security requirements, and go\-to\-market (GTM) pilot rollouts. Establish the core quantitative Key Performance Indicators (KPIs) that track early feature adoption, and conduct open, evidence\-driven retrospectives on experimental features to constantly iterate on team strategy
What You’ll Need
Minimum Qualifications
- 6\+ years of experience in product management or a closely related technical product\-building role with a Bachelor’s degree; OR 4\+ years of experience with an Advanced Degree
- Demonstrated track record managing B2B SaaS applications, enterprise workflow automation software, data\-intensive products, or corporate business tools
- Strong conceptual understanding of the modern applied AI landscape, including Large Language Model (LLM) capabilities, multi\-agent orchestration, and human\-in\-the\-loop design patterns. You know what AI *can* do for a user, and how to design experiences around it
- Proven experience translating highly ambiguous, unstructured user pain points into structured technical requirement documentation, validation roadmaps, and testable hypotheses
- Exceptional product craft with a deep focus on user psychology, interface transparency, and building high\-trust solutions in risk\-averse environments
Preferred Qualifications
- Ability to understand how modern software products are structured, including APIs, front\-end experiences, back\-end services, data flows, AI/ML services, and integrations, and to use modern AI tools to explore, prototype, and work effectively with engineering
- Advanced degree
- Background working within deeply regulated domains (e.g., corporate accounting, financial auditing, compliance reporting, risk management, legal operations, or ESG/sustainability tracking)
Working Conditions \& Travel Requirements
- Ability to travel up to 20% to facilitate customer discovery workshops, align with internal distributed product teams, and represent Workiva at key industry conferences
- Reliable internet access during any remote work outside a Workiva office
How You’ll Be Rewarded
✅ Salary range in the US: $129,000\.00 \- $207,000\.00
✅ A discretionary bonus typically paid annually
✅ Restricted Stock Units granted at time of hire
✅ 401(k) match and comprehensive employee benefits package
The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.
Why Join Workiva
Workiva is the platform designed to bring confidence, control, and a competitive edge to the world’s most complex organizations. Our AI\-powered platform unifies finance, risk, and sustainability on a single, secure foundation—ensuring data is trusted, traceable, and ready to act on. With an unbroken path from source to output, leaders gain confidence in their numbers, visibility into current and emerging risks, and the ability to move with speed and precision in a constantly changing world.
At Workiva, you’ll bring technology to market that executives, boards, and regulators depend on. The work you do here helps organizations navigate uncertainty, maintain trust, and make decisions that stand up to scrutiny. If you’re energized by meaningful challenges, inspired by collaborative teams, and motivated to help organizations turn uncertainty into advantage, we’d love to meet you.
Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic.
Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email talentacquisition@workiva.com .
Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards.
*Workiva supports employees in working where they work best \- either from an office or remotely from any location within their country of employment.*
\#LI\-LP1
Salary Context
This $129K-$207K range is below the median for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Workiva, 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 Required
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 $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($168K) sits 22% below the category median. Disclosed range: $129K to $207K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Workiva AI Hiring
Workiva has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $207K - $207K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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