Hands-on with LLMs and agentic coding tools; builds AI agents, knowledge graphs, and focuses on rapid prototyping and inference-cost-aware deployment.
About the Role
Lead a team building the B2B data graph and intelligence for ZoomInfo AI agents, combining classical ML, data science, and LLM/agentic systems. Own strategy, end-to-end delivery, evaluation, and inference cost while hiring and mentoring senior ML/data engineers and shipping production systems.
Job Description
Role
Director-level technical leader responsible for the B2B data graph strategy that powers ZoomInfo AI agents. The role blends classical machine learning and data science with LLM and agentic systems, and requires shipping code, setting technical standards, and growing senior engineers into technical leaders.
Key Responsibilities
- Ship code alongside the team and prototype independently to validate ideas; set team practices for using agentic coding tools with precise specs and rigorous review.
- Own delivery end-to-end: problem framing, serving, on-call, long-tail graph coverage, and user memory that separates user-supplied context from system-of-record data.
- Choose appropriate methods (classical ML, language models, or code) for tasks such as sparse-company revenue estimation, entity resolution, and semantic intent modeling.
- Define correctness for agent outputs by building evaluation datasets, regression gates, and experiment designs; ensure leakage-safe validation and calibrated evaluation.
- Own inference cost, latency, and capacity decisions including build-vs-buy and distillation trade-offs.
- Hire, grow, and mentor machine learning engineers, data scientists, and research engineers; develop senior engineers into technical leaders.
- Collaborate across product, platform, security, and legal; present results and limits to executives and recommend against launch when appropriate.
Requirements
Must-have
- Significant experience building production machine learning systems and leading engineers while remaining hands-on and shipping alongside them.
- Proven track record of hiring and developing senior ML engineers and data scientists.
- Actively hands-on: shipping code, building prototypes, and using agentic coding tools daily with rigorous review.
- Deep expertise in classical machine learning and data science (supervised learning, feature engineering, statistical inference, experiment design) and strong SQL skills.
- Production experience with LLM and agentic systems, including setting evaluation bars with leakage-safe validation, calibration, and validated LLM judges.
- Demonstrated cost and capacity decision-making for model serving, including migrations to distilled or self-hosted models and communicating trade-offs to executives.
Preferred
- Entrepreneurial experience (founding a company or early-stage engineering experience bringing a product to paying customers).
- Experience with propensity modeling, ranking/retrieval, clustering, or large-scale entity resolution.
- Experience with web-scale language processing over multilingual/noisy text, knowledge graphs, or user memory for agents.
- Experience with post-training/distillation, open-weight model serving, or AI governance and safety practices (e.g., ISO/IEC 42001, NIST AI RMF).
Compensation & Benefits
- US base salary range provided: $233,100 — $366,300 USD. Additional compensation such as bonus, commission, equity, and other benefits may apply.
- Company mentions comprehensive benefits and holistic mind, body, and lifestyle programs.
Location & Remote
- Role tagged as hybrid (#LI-hybrid). Specific office location not listed in the posting; compensation example provided is the US base salary.