Note: The job is a remote job and is open to candidates in USA. ZoomInfo is hiring an Applied AI Engineer for its Core Data team. The role builds and operates production infrastructure for company and contact agents, including evaluation and benchmarking systems, data pipelines, evidence adapters, deployment tooling, and self-service review tools.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- Holistic mind, body and lifestyle programs designed for overall well-being
- Additional compensation such as Bonus, Commission, Equity may also apply
Company Overview
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