Director, Applied AI

Role overview

This director-level position leads the team responsible for the intelligence layer that AI agents reason over within a B2B context, covering what is known about companies, the people at them, their relationships, and buying behavior. The role owns the B2B data graph strategy end-to-end, blending classical machine learning, data science, large language models, and agentic systems while treating evaluation and inference cost as core disciplines. The leader stays close to the code, shipping alongside senior engineers and setting the technical bar for the group.

Responsibilities

- Ship code alongside the team, prototype independently to validate or kill ideas, and review code as a peer while setting standards for agentic coding tools through precise specifications and rigorous review - Own end-to-end delivery from problem framing through production serving and on-call, including long-tail graph coverage and agent user memory that separates user-supplied context from system-of-record data - Select the right approach for each problem, choosing among classical machine learning, language models, or code for tasks such as sparse-company revenue estimation, entity resolution, and semantic intent modeling, and base decisions on measured evidence while stopping work that will not pay off - Define what it takes to claim an agent's output is correct rather than merely that it ran, by constructing evaluation datasets, regression gates, and experiment designs - Own inference cost, latency, and capacity decisions, including build-versus-buy and distillation trade-offs, since models too expensive to run everywhere do not count as results - Hire and develop machine learning engineers, data scientists, and research engineers, grow senior engineers into technical leaders, and partner across product, platform, security, and legal while presenting results and their limits to executives, including when a system is not ready to launch

Requirements

- Significant experience building production machine learning systems and leading the engineers who build them by shipping alongside them, with demonstrated capability valued more than tenure - Track record of hiring and developing senior machine learning engineers and data scientists against a high bar - Hands-on coding today, including building prototypes independently and using agentic coding tools daily with rigorous review - Deep classical machine learning and data science expertise covering supervised learning, feature engineering, statistical inference, experiment design, and strong SQL, alongside production LLM and agentic systems, with the judgment to choose between them and an evaluation bar that uses leakage-safe validation, calibration, and validated LLM judges - Record of cost and capacity decisions for model serving, such as migrating workloads from hosted to distilled or self-hosted models with measured savings, plus executive reporting that states limits and recommends against launch when warranted

Nice to have

- Entrepreneurial experience such as founding a company or taking a product from inception to paying customers as a founding or early engineer - Experience with propensity modeling, ranking and retrieval, clustering, or entity resolution at scale - Work on web-scale language processing over multilingual noisy text, knowledge graphs, or user memory for agents - Familiarity with post-training and distillation, open-weight model serving, or AI governance and safety practices such as ISO/IEC 42001 and the NIST AI RMF

Benefits and work setup

- Hybrid work arrangement - US base salary range of $233,100 to $366,300, with additional compensation such as bonus, commission, and equity potentially available - Comprehensive benefits program with holistic mind, body, and lifestyle programs designed for overall well-being