Senior Machine Learning Engineer

ZoomInfo is where careers accelerate. They are seeking a Senior Machine Learning Engineer to own critical projects that enhance the quality and reliability of core datasets, working at the intersection of AI and massive-scale data processing.

Responsibilities:

• Invent and productionize Transformer/RAG/Graph RAG architectures that surface the right contact, company, or insight while driving quantization, distillation, and SLM fine-tuning (GTE-Qwen, modernBERT) so models stay fast and affordable at petabyte scale
• Prototype and launch hybrid dense/sparse retrieval pipelines on vector DBs to build language-agnostic clustering and classification systems that power our intelligence layer
• Own high-recall NER models that tag people, orgs, locations, and industry-specific entities across multi-language text, extracting structured insights from web data to improve our signal detection capabilities
• Build cross-dataset entity-resolution frameworks that dedupe and merge hundreds of millions of fragmented company and person records with sub-second latency, creating enriched, unified entities enhanced with knowledge-graph signals
• Design and implement agentic workflows with robust evaluation frameworks focused on NER and entity resolution tasks, including large-scale A/B and back-testing plans that close the loop from experiment to KPI uplift
• Scale ML solutions and drive cross-functional impact by partnering with ML engineers to ensure production reliability, translating product goals into measurable ML KPIs, and influencing roadmap and investment decisions while mentoring junior scientists and engineers
• Drive end-to-end project ownership from problem definition through deployment, collaborating closely with engineering and product teams to understand business requirements and translate them into scalable ML solutions that enhance foundation data quality across company firmographics, professional demographics, C-suite profiles, and web-extracted signals

Requirements:

• 6+ years hands-on ML/NLP experience (or 3+ years post-PhD/Master's) with at least two delivered, revenue-impacting products in production environments
• Deep expertise in modern AI architectures including transformer stacks (BERT/GPT/T5), RAG systems, vector-based information retrieval, and latency/throughput optimization techniques
• Proven track record building NER or entity-resolution systems at 100M+ record scale with experience in record linkage, data deduplication, and knowledge-graph integration
• Strong applied research capabilities (PyTorch or TensorFlow) paired with software-engineering rigor (Python, Go/Java a plus) and familiarity with embedding models and vector search technologies
• Executive communication skills with ability to persuade technical and non-technical audiences through data-driven storytelling, comfortable owning strategy, budget, and cross-functional collaboration