Principal Applied AI Engineer - Entity Agents
Remote · Waltham · USD 171,500-269,500 · 5+ years experience · Full-time · Remote · Posted Oct 2, 2026
At a glance
- Experience
- 5+ years experience
- Employment
- Full-time
- Workplace
- Remote
- Location
- Remote · Waltham
- Salary
- USD 171,500-269,500 · OTE
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.
About the role
- ZoomInfo is hiring a Senior Applied AI Engineer to build and own the machinery that runs company and contact entity agents at production volume.
- You will complete the entity-agent pipeline, build evidence adapters, run benchmark and golden-gate systems on a schedule, and deploy agents onto ZoomInfo's Agentic Platform.
Responsibilities
- Run the benchmark and golden-gate system, moving judge runs, golden-gate results, arms scorecards, and cost per record to a scheduled system against staging.
- Complete the entity-agent pipeline by building missing stages such as enricher fan-out, location sets, and hierarchy batch files, and migrate classifiers to the shared runner.
- Build evidence adapters for registries, live web, mail-tenant signals, and external research vendors behind a single envelope with a replayable vendor store and a per-vendor kill switch.
- Deploy onto the ZoomInfo Agentic Platform by carrying company and contact agents through the production plan and building the ingest adapter side.
- Make the loop self-service by building the review queue, tagging tools, blast-radius viewer, and batch runners for researchers and product managers.
- Keep agent surfaces alive by owning the health and deployment of the company classifier, contact classifier, agents hub, and judge platform inside internal tooling.
- Instrument everything, tracking cost per call, per row, per stage, quality per version, and setting up alerts.
Requirements
- 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 evaluation harnesses.
- Strong Python and TypeScript skills, with comfort across an API layer, a batch runner, and a small web UI for internal users.
- Experience building platforms that others operate, such as adapters, runners, queues, or internal tools designed for replay, idempotency, and observability.
- Working knowledge of data pipelines and entity resolution, including ingestion, matching, serving, and the cost of false merges versus missed matches.
- A track record of taking a prototype to production while treating test sets and benchmarks as the contract.
- Instrumentation by default, knowing the cost and latency budget of every shipped system.
- Strong bias for action and tenacity to find endpoint owners and unblock runners.
Preferred qualifications
- 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
- Comprehensive benefits
- Holistic mind, body and lifestyle programs designed for overall well-being
- Bonus
- Commission
- Equity
Workplace
- Remote or Waltham location.
Skills
- Python
- TypeScript
- LLM Systems
- Agent Loops
- Retrieval
- Structured Output
- Evaluation Harness
- Data Pipelines
- Entity Resolution
- Claude
- Agent Frameworks
