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About This Role
ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won't just contribute. You'll make things happen–fast.
Principal Product Manager, Person Data and AI Evaluations
ZoomInfo | Product | Core Data
About ZoomInfo
ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of
your life. You'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins.
With tools that amplify your impact and a culture that backs your ambition, you won't just contribute, you'll
make things happen, fast.
The Opportunity
ZoomInfo's Core Data team builds and maintains the person and company data that powers the Go-To-
Market Intelligence Platform: hundreds of millions of contact records, resolved to the right person at the right
company, kept accurate, and delivered to more than 35,000 customers.
That data has always been produced by deterministic pipelines: take many competing sources, weight them,
decay them, and select a winner per attribute. That model is being replaced. Core Data is moving to an
inference-default operating model, where an agent reads the full body of evidence for a record, applies our
business policy, proposes the answer, and a deterministic verification layer decides whether it lands.
Selection logic goes away; verification and evaluation are what remain. The product manager's job changes
with it. Instead of writing requirements for hand-built selection rules, you run the evaluation engine that
decides whether the agent's output is good enough to publish, and you make it better every week.
We are hiring a Principal Product Manager to own Person Data outcomes end to end and to build the AI
evaluation discipline that the whole Core Data team will run on. You inherit a live portfolio: person data
quality (removing bogus or outdated executive contacts, unlinking contacts from the wrong company, title
classification, email deliverability), person coverage and extraction, and the privacy roadmap for person data.
You will carry that portfolio through the pivot from rule-based selection to agent-adjudicated records, and
you will define how we know the new system is right.
This is a role for someone who has shipped data pipelines and entity resolution at scale and who is already
using AI every day to make data systems better. You should be as comfortable reading a golden set and an
eval report as you are writing a roadmap, and you should be excited that our product managers commit code
and work alongside agents as a normal part of the job.
What You'll Do
Own Person Data end to end. Set the strategy, roadmap, and monthly priorities for ZoomInfo's contact data:
accuracy, coverage, freshness, and compliance. Own the outcomes and the metrics that prove them, from
cleaning up bogus executive contacts and verifying employment through leadership extraction and person
location. Partner with the Person Data product manager already on the team and with Privacy, Trust and
Identity engineering to deliver the roadmap.
Build and run the AI evaluation engine. Define what "correct" means for each attribute the agent emits.
Curate golden sets and trap records with our research team, stand up LLM-as-judge gates, set the confidence
thresholds that route a record to auto-accept, auto-reject, or human adjudication, and track precision, recall,
and drift in production. Every model or prompt change to the person pipeline ships through the gate you own.
Drive the pivot from rules to agents. Move person attributes, one at a time, from deterministic selection to
inference over full evidence. Write the policy clauses the agent follows, the grading guides research uses to
score it, and the acceptance rules the pipeline enforces. Decide the order, prove each step in shadow mode
against the gold set, and retire the legacy logic when the numbers say it is safe.
Bring AI into the pipeline without tanking the data. Introduce models where they earn their place and keep
classical tooling where it wins: normalization, string similarity, registry lookups, deterministic rules. Reason
explicitly about cost per row, latency, determinism, and auditability. Know when a bigger model is the wrong answer.
Treat entity resolution as the hard core. Person-to-company matching, wrong-company links, duplicate
people, and identity across sources are the problems that make or break contact data. Bring a clear mental
model for canonical identity, match confidence, and the cost asymmetry between a false merge and a missed match.
Own the privacy roadmap for person data. Own the privacy roadmap for person data — suppression, opt-
out, and notification — with Legal and Privacy.
Build with the team, hands on. Prototype adjudication agents, evals, and analysis in code with the AI
engineer and data science. Commit to the shared repository. Use Claude and agentic tooling daily to inspect
results, tune prompts, and verify output so that no single absence blocks an iteration cycle.
Drive cross-functional execution and communicate strategically. Act as the hub between Person Data
engineering, Match, Research, Data Science, Web Data acquisition, Privacy, and the application teams that
consume contact data. Represent the roadmap and its rationale clearly to product and data leadership, and
write the release notes and customer-facing narrative with Product Marketing.
What You'll Bring
8+ years of product management experience, with meaningful time owning data products or data
platforms at scale
- Hands-on experience with data pipelines, ideally incremental or streaming rather than batch rebuilds,
and a working understanding of how records move from ingestion through processing to a served dataset
- Direct experience with entity resolution, record linkage, or matching systems: canonical IDs, match
confidence, survivorship rules, and the trade-offs between precision and recall
- Demonstrated, current use of AI to improve data systems, not just AI as a feature: you have brought
an LLM into a production data process and can describe how you kept accuracy from degrading
- Experience designing evaluations for model output: golden sets, precision and recall, LLM-as-judge,
human-in-the-loop routing, and production drift monitoring
Comfort working in code: you can read a pipeline, write a prompt and a validator, run an analysis in
SQL or Python, and commit alongside engineers
Experience partnering closely with engineering and data science on complex, data-intensive systems,
and a track record of influencing technical direction without formal authority
- Strong bias for action and a demonstrated ability to drive focus and finish work in a fast-moving,
ambiguous, and frequently chaotic environment
- Excellent written and verbal communication with the ability to move between technical depth and executive narrative.
Preferred
Experience with contact, person, or identity data, or with B2B company data
- Familiarity with privacy regulation as it applies to published personal data (GDPR, CCPA, DNC, and
international notification regimes)
- Experience with Claude or comparable frontier models, agent frameworks, and eval harnesses
- Background at an AI research lab or an AI-native data company
Who You Are
AI-native, not AI-curious. You use AI tools every day and you have opinions about where inference beats
hand-built logic and where it does not. You would rather over-index on AI fluency and learn our data than the reverse.
An entity-resolution thinker. You see a data quality problem and immediately ask which entity it is about,
what the canonical form is, and what evidence would settle it.
An evaluator by instinct. You do not ship a model change without a gold set, a threshold, and a plan for what
happens at ten times the volume. "We'll A/B test it" is not an eval plan.
A builder. You prototype, you read code, you commit. You are comfortable being the person who inspects
agent output at nine in the morning and tunes the prompt by ten.
Tenacious. ZoomInfo is fast, distributed, and messy. Many teams touch person data. You go find the people
you need, you drive to a decision, and you keep the team focused on the few things that matter while
everything else flies past.
One team. Honest, has the team's back, celebrates wins and losses together.
The Environment
You report to the Senior Manager of Product for Core Data and sit inside the Core and Global Data
organization under the Chief Data Officer. Your day-to-day partners are Person Data engineering, the Match
team, the Research organization that produces golden records, Data Science, and the AI engineer building the
shared model-and-eval platform. Expect interesting problems: when is a "Vice President" an executive and
when is it a mid-level title? How do you unlink wrong-company contacts at scale without losing the right
ones? How do you prove an agent is more accurate than the rules it replaces, and how do you keep proving it after launch?
About us:
ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.
ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.
ZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here.
ZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs, and does not discriminate based on protected status. We welcome all applicants and are committed to providing equal employment opportunities regardless of sex, race, age, color, national origin, sexual orientation, gender identity, marital status, disability status, religion, protected military or veteran status, medical condition, or any other characteristic protected by applicable law. We also consider qualified candidates with criminal histories in accordance with legal requirements.
For Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. ZoomInfo does not administer lie detector tests to applicants in any location.
Salary Context
This $151K-$238K range is above the median for AI Product Manager roles in our dataset (median: $194K across 123 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 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At ZoomInfo, 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 $220,200 based on 947 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $151K to $238K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
ZoomInfo AI Hiring
ZoomInfo has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Bethesda, MD, Waltham, MA, US, Boston, MA, US. Compensation range: $238K - $303K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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