Principal Applied AI Engineer - Entity Agents at ZoomInfo Technologies

Job description

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. Senior Applied AI Engineer, Entity Agents 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 the company and contact data behind the Go-To-Market Intelligence Platform: hundreds of millions of records, resolved to the right entity, kept accurate, and delivered to more than 35,000 customers. The way that data is produced is changing. For years it came from deterministic pipelines: many sources, weighted and decayed, one winner per attribute. Core Data is now moving to AI-native data management, where an agent pursues the evidence for an entity, decides every attribute over all of it, and records why. The company and contact agents already exist as working, measured prototypes. What does not exist yet is the machinery that runs them at production volume, proves every version before it ships, and lets researchers and product managers operate them without an engineer in the loop. We are hiring a Senior Applied AI Engineer to build and own that machinery. You will complete the entity-agent pipeline, build the evidence adapters behind a single envelope, run the benchmark and golden-gate system on a schedule, build the review and batch tooling the team operates, and carry the agents onto ZoomInfo’s Agentic Platform. You will work with the Principal AI Engineer who owns the benchmark harness and productionization, the product manager who owns evaluation and policy, and the company and person engineering teams that own the pipelines the agents feed. You will not be the only person who can do this work; you will be the second, so that neither of you is a single point of failure. This is a role for someone who has shipped LLM systems in production with evaluations, builds platform for other people to operate, and can work inside a large-scale data pipeline. You should be as comfortable writing an adapter with a replayable store and a kill switch as you are reading a miss analysis with a product manager, and you should like that the prototype you take to production was built by a PM who commits code. What You’ll Do Run the benchmark and golden-gate system. Move judge runs, golden-gate results, arms scorecards, and cost per record from a laptop to a scheduled system against staging. Every agent version is gated automatically, with variance and drift tracked per release. The evaluation product manager sets the thresholds; you make them enforceable. Complete the entity-agent pipeline. Build the stages that do not exist yet: enricher fan-out, location sets, and hierarchy batch files. Migrate the classifiers to the shared runner with the Principal AI Engineer so they run at production scale. Measure throughput and unit cost per stage on named cohorts, so the cost model comes from instrumentation rather than estimates. Build the evidence adapters. Registries, live web, mail-tenant signals, and external research vendors all sit behind one envelope with a replayable vendor store, measured lift per shape, a per-vendor kill switch, and at least two sources per shape. Vendors are swappable by design. Deploy onto the ZoomInfo Agentic Platform. Carry the company and contact agents through the production plan onto ZAP, build the ingest adapter side (envelope to match service and bulk intake), and keep a rollback path per version. Keep the DOH implementation and the platform implementation upgraded in lockstep, enforced by tooling. Make the loop self-service. Build the review queue, tagging tools, blast-radius viewer, and batch runners that researchers and product managers use without an engineer. Measure review yield and golden-record production. Keep the agent surfaces alive. Own the health and deployment of the company classifier, contact classifier, agents hub, and judge platform inside our internal tooling, so the team’s iteration cycle never waits on one person. Instrument everything. Cost per call, per row, per stage; quality per version; alerts before a human notices. What You’ll Bring 5+ years of software engineering experience, including at least 2 years shipping LLM-based systems to production: agent loops, tool use, retrieval, structured output, and the evaluation harness that proved them Strong Python and TypeScript, and comfort across an API layer, a batch runner, and a small web UI for internal users Experience building platform others operate: adapters, runners, queues, or internal tools designed for replay, idempotency, and observability Working knowledge of data pipelines and entity resolution: how records move through ingestion, matching, and serving, and what a false merge costs versus a missed match A track record of taking a prototype to production without losing what the prototype proved, treating test sets and benchmarks as the contract Instrumentation by default: you know the cost and latency budget of every system you have shipped Strong bias for action and the tenacity to find an endpoint owner, get a decision, and unblock a runner in a fast-moving, distributed organization Preferred Experience with B2B company or contact data, identity or registry data, or entity resolution at scale Experience with Claude or comparable frontier models, agent frameworks, and eval harnesses Experience deploying agents onto an internal agent platform or orchestration layer Background at an AI-native startup, a forward-deployed or applied AI team, or a data infrastructure company Who You Are A builder who ships. You pick the boring technology when it wins and you would rather have a running adapter than a design doc. An evaluator by instinct. You do not release a version without a test set, a threshold, and a number for what it costs. A platform thinker. You build things that other people run, and you design for the day the vendor goes down. Comfortable in the data. You can read a pipeline, reason about matching, and explain why a wrong merge is worse than a missed one. Tenacious. ZoomInfo is fast, distributed, and messy. You go find the people you need and drive the work to done. 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, with the Principal AI Engineer as your technical lead, and sit inside the Core and Global Data organization under the Chief Data Officer. Your day-to-day partners are the Principal AI Engineer, the product manager who owns evaluation and data policy, the Company and Person Data product managers, the research team that grades and rules, the agentic platform team, and company and person engineering. Expect interesting problems: how do you run a judge over a million records a day for cents, not dollars? How do you make a vendor replaceable without a rewrite? How do you let a researcher run a batch and rule on the misses without paging you? About ZoomInfo 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 proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs. Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive. In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here . Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply. $171,500 — $269,500 USD About us: ZoomInfo (NASDAQ: GTM), the all-in-one AI GTM platform, enables sales, marketing, and customer success teams to execute their go-to-market strategy with confidence. Powered by the industry's most comprehensive B2B data, including more than 100 million companies, 500 million contacts, and billions of signals, ZoomInfo delivers the intelligence, automation, and integrations that modern revenue teams need to identify, engage, and convert their best buyers. 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.

Requirements

Must-haves

ZoomInfo | Product | Core Data
What You’ll Do
Make the loop self-service. Build the review queue, tagging tools, blast-radius viewer, and batch runners that researchers and product managers use without an engineer. Measure review yield and golden-record production.
Instrument everything. Cost per call, per row, per stage; quality per version; alerts before a human notices.
What You’ll Bring
5+ years of software engineering experience, including at least 2 years shipping LLM-based systems to production: agent loops, tool use, retrieval, structured output, and the evaluation harness that proved them
Strong Python and TypeScript, and comfort across an API layer, a batch runner, and a small web UI for internal users
Experience building platform others operate: adapters, runners, queues, or internal tools designed for replay, idempotency, and observability
Working knowledge of data pipelines and entity resolution: how records move through ingestion, matching, and serving, and what a false merge costs versus a missed match
A track record of taking a prototype to production without losing what the prototype proved, treating test sets and benchmarks as the contract
Instrumentation by default: you know the cost and latency budget of every system you have shipped
Strong bias for action and the tenacity to find an endpoint owner, get a decision, and unblock a runner in a fast-moving, distributed organization
A builder who ships. You pick the boring technology when it wins and you would rather have a running adapter than a design doc.
An evaluator by instinct. You do not release a version without a test set, a threshold, and a number for what it costs.
A platform thinker. You build things that other people run, and you design for the day the vendor goes down.
Comfortable in the data. You can read a pipeline, reason about matching, and explain why a wrong merge is worse than a missed one.
Tenacious. ZoomInfo is fast, distributed, and messy. You go find the people you need and drive the work to done.
One team. Honest, has the team’s back, celebrates wins and losses together.
The Environment

Nice-to-haves

Experience with B2B company or contact data, identity or registry data, or entity resolution at scale
Experience with Claude or comparable frontier models, agent frameworks, and eval harnesses
Experience deploying agents onto an internal agent platform or orchestration layer
Background at an AI-native startup, a forward-deployed or applied AI team, or a data infrastructure company

Application prep

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Benefits

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