What this case covers: You’ll be asked to design, reason about, and evaluate a production-grade AI/ML solution that improves ZoomInfo’s B2B data quality and downstream go‑to‑market intelligence. Expect a practical, metrics‑driven discussion that mirrors real work: deduplicating and enriching company/contact records from heterogeneous, web‑extracted and partner sources; extracting entities and signals from conversation transcripts; and deploying the solution with MLOps rigor. This reflects reported ZoomInfo interview patterns emphasizing hands‑on case/problem solving alongside cultural fit. ([interviewquery.com](https://www.interviewquery.com/interview-guides/zoominfo-machine-learning-engineer?utm_source=chatgpt.com)) Scenario prompt used by interviewers: “Given noisy company and contact data from multiple sources plus sample sales-call transcripts, design an AI system that: (1) resolves entities (person/company) into a unified graph; (2) extracts titles/roles, relationships, and buying intent from calls; and (3) serves high‑quality enrichment to product features and customer workflows.” Conversation data is motivated by ZoomInfo’s Chorus.ai acquisition and integrations, which surface ML‑derived insights from sales calls. ([zoominfotechnologiesinc.gcs-web.com](https://zoominfotechnologiesinc.gcs-web.com/news-releases/news-release-details/zoominfo-announces-its-first-integrations-chorusai?utm_source=chatgpt.com)) Focus areas and what good looks like: - Problem framing and assumptions: How you define matching objectives, success metrics, risks (PII/privacy), and failure modes in a large B2B data platform. Briefly articulate offline vs. online KPIs (e.g., precision/recall/F1 for linkage; latency, coverage, cost for serving). Context: ZoomInfo is a large B2B data SaaS with compliance considerations. ([en.wikipedia.org](https://en.wikipedia.org/wiki/ZoomInfo?utm_source=chatgpt.com)) - Entity resolution on B2B records: Propose blocking strategies and candidate generation (rules + embeddings), pairwise scoring (GBDT/NN), thresholding with cost‑weighted metrics, and graph consolidation. Data quality and de‑dupe are first‑class concerns in ZoomInfo’s stack (e.g., RingLead acquisition for orchestration/cleansing). ([zoominfotechnologiesinc.gcs-web.com](https://zoominfotechnologiesinc.gcs-web.com/news-releases/news-release-details/zoominfo-acquires-ringlead-data-orchestration-leader-help?utm_source=chatgpt.com)) - NLP/LLM for unstructured inputs: Use NER, relation extraction, and intent classification on web text and call transcripts; discuss embeddings, vector search, and evaluation for language/format variance—aligning with ZoomInfo role descriptions highlighting embeddings, NER, vector search, and entity resolution. ([echojobs.io](https://echojobs.io/job/zoominfo-machine-learning-engineer-iii-dv9k0?utm_source=chatgpt.com)) - Productionization and reliability: Design feature stores, model/version management, canary deploys, drift/quality monitoring, labeling feedback loops, and guardrails for LLM outputs; note cost/performance trade‑offs and SLAs. - Business impact orientation: Tie model choices to measurable GTM outcomes (enrichment match‑rate lift, lead routing accuracy, reduced duplicate creation). Interviewers value initiative, teamwork, accountability, and results—commonly echoed in ZoomInfo job posts and interview guides. ([echojobs.io](https://echojobs.io/job/zoominfo-machine-learning-engineer-iii-dv9k0?utm_source=chatgpt.com), [interviewquery.com](https://www.interviewquery.com/interview-guides/zoominfo-machine-learning-engineer?utm_source=chatgpt.com)) Format and pacing (typical): - 5 min: Clarify objectives, constraints, data sources, and consumers (internal services, customer‑facing APIs). - 35–40 min: Whiteboard end‑to‑end design (ingest → candidate gen → matching/scoring → consolidation → serving), LLM/NLP pipeline for transcripts, metrics and experiments, privacy/compliance approach. - 10–15 min: Deep‑dive challenges (label scarcity, multilingual noise, backfills, human‑in‑the‑loop QA, incident response) and trade‑offs. - 5 min: Impact summary and next steps (MVP scope, success criteria, risks). How it reflects ZoomInfo’s style: Practical casework tied to their data‑centric GTM platform and conversation intelligence surface area (Chorus); emphasis on data quality/orchestration (RingLead); expectation that AI Engineers think product‑in and ship production systems with clear business value. ([zoominfotechnologiesinc.gcs-web.com](https://zoominfotechnologiesinc.gcs-web.com/news-releases/news-release-details/zoominfo-announces-its-first-integrations-chorusai?utm_source=chatgpt.com), [echojobs.io](https://echojobs.io/job/zoominfo-machine-learning-engineer-iii-dv9k0?utm_source=chatgpt.com))