At ZoomInfo, we encourage creativity, value innovation, demand teamwork, expect accountability and cherish results. We value your take charge, take initiative, get stuff done attitude and will help you unlock your growth potential. One great choice can change everything. Thrive with us at ZoomInfo.
At ZoomInfo we turn billions of data points into instant go-to-market answers for 40,000+ customers. To keep that edge, we’re building an elite Applied AI platform that scales from first experiment to planet-scale production—securely, cost-effectively, and fast. Join us as a Senior Machine Learning Engineer and own the MLOps & LLMOps backbone that powers our retrieval, recommendation, and knowledge-graph products.

ABOUT US:

ZoomInfo (NASDAQ: ZI) 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 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

• Architect the end-to-end MLOps / LLMOps stack (Kubernetes, Ray, Argo, Terraform, MLFlow, Feature & Model Stores) for training, fine-tuning, and serving LLMs, RAG pipelines, NER, and entity-resolution models at multi-billion-record scale.
• Design cost-aware training & inference workflows—automatic mixed precision, quantization, distillation, dynamic batching, on-demand GPU/CPU autoscaling—to cut $/call and CO₂ while meeting 99.9% SLAs.
• Build iron-clad evaluation & safety frameworks: red-teaming, hallucination scoring, PII/toxicity filters, guardrail policies, shadow deployments, and continuous regression tests.
• Fine-tune embedding models and pre-train domain-specific LLMs on ZoomInfo corpora; integrate with vector databases (Pinecone, Milvus, OpenSearch) to power RAG search and recommender systems.
• Prototype and benchmark emerging AI/infra tech (parameter-efficient tuning, retrieval frameworks, serverless GPUs) against incumbent solutions; present clear “buy-build-borrow” recommendations.
• Model, build, and optimize knowledge graphs—schema design, entity linking, incremental updates—to supercharge retrieval and recommendation accuracy.
• Profile and re-architect RAG, NER, and entity-resolution services for sub-100 ms latency, 5× QPS jumps, and zero-downtime deploys across global regions.
• Lead multi-team CI/CD best practices—blue/green, canary, GitOps—and large-scale A/B tests to quantify revenue impact.
• Mentor engineers, publish internal playbooks & external blogs, and speak at industry forums to cement ZoomInfo’s technical leadership.