About this role
Lead the team building the B2B data-graph intelligence used by ZoomInfo’s AI agents to reason about companies, people, relationships and buying signals. Own the graph strategy end to end, combining classical machine learning, data science, LLMs and agentic systems while remaining hands-on with code.
Responsibilities
- Ship code, independently prototype ideas, review code and establish rigorous practices for agentic coding tools.
- Own delivery from problem framing through serving and on-call. Address long-tail graph coverage and agent user memory while keeping user-supplied context separate from system-of-record data.
- Select methods based on evidence for problems including sparse-company revenue estimation, entity resolution and semantic intent modeling. Build evaluation datasets, regression gates and experiments that establish whether agent outputs are correct.
- Manage inference cost, latency and capacity, including build-versus-buy and model-distillation decisions. Hire and develop machine learning engineers, data scientists and research engineers; collaborate with product, platform, security and legal teams, and communicate results and limitations to executives.
Required qualifications
Significant experience building production machine learning systems and leading engineers while shipping alongside them; demonstrated capability matters more than years. A record of hiring and developing senior technical staff, hands-on coding and prototyping, and daily use of agentic coding tools with rigorous review is required. Bring deep expertise in supervised learning, feature engineering, statistical inference, experiment design and SQL alongside production LLM and agentic systems. Be able to establish evaluation standards using leakage-safe validation, calibration and validated LLM judges, and make measured model-serving cost and capacity decisions.
Preferred qualifications
Entrepreneurial experience; propensity modeling, ranking, retrieval, clustering or large-scale entity resolution; multilingual web-scale language processing, knowledge graphs or agent user memory; post-training, distillation, open-weight model serving, or AI governance and safety practices such as ISO/IEC 42001 and NIST AI RMF.
This full-time role lists Bethesda, Vancouver (Washington), Waltham and remote options in Massachusetts, Maryland and Washington; the posting is tagged hybrid. The US base salary is $233,100–$366,300 annually, with location and qualifications affecting the offer. Bonus, commission, equity and benefits may also apply.