Director, Applied AI

Accountabilities:

• Lead the strategy and development of a comprehensive B2B data graph, extending coverage to organizations with limited public information.
• Own the end-to-end data graph lifecycle, from training data and modeling through production model serving.
• Develop systems that enable AI agents to reason over accurate information about companies, people, relationships, and purchasing activity.
• Lead initiatives around agent memory, clearly distinguishing user-provided information from data already maintained by authoritative systems.
• Select the most appropriate technical approach for each problem, including classical machine learning, language models, agentic systems, and code-based solutions.
• Use measured evidence to prioritize high-value initiatives and discontinue approaches that are unlikely to deliver sufficient impact.
• Establish evaluation standards for machine learning models and AI agents, ensuring quality claims are measurable and trustworthy.
• Build evaluation datasets, regression gates, experiment frameworks, and validation processes for continuous model improvement.
• Own inference cost, latency, and capacity alongside model quality and system performance.
• Make informed build-versus-buy decisions and assess opportunities for model distillation and optimization.
• Hire, mentor, and develop machine learning engineers, data scientists, and research engineers.
• Help senior engineers grow into technical leadership roles while maintaining a strong engineering culture.
• Remain hands-on by writing production code, developing prototypes, and working directly alongside the engineering team.
• Establish standards for the effective use of agentic coding tools, combining precise specifications with rigorous code review.
• Collaborate with product, platform, security, legal, and other stakeholders to align technical work with broader business and operational requirements.
• Communicate technical results, limitations, trade-offs, and risks clearly to executive stakeholders.
• Set and maintain a high technical bar for production machine learning, data science, AI agents, and supporting infrastructure.

Requirements:

• Significant demonstrated experience building and shipping production machine learning systems, with hands-on technical leadership.
• Proven experience hiring, mentoring, and developing senior machine learning engineers and data scientists.
• Current hands-on engineering experience, including writing code, building prototypes, and contributing directly to production systems.
• Strong expertise in classical machine learning and data science, including supervised learning, feature engineering, statistical inference, and experiment design.
• Strong SQL skills and the ability to work effectively with complex datasets and data-intensive systems.
• Production experience with LLMs and agentic AI systems, including the judgment to determine when these approaches are appropriate.
• Experience establishing rigorous model evaluation standards, including leakage-safe validation and calibration.
• Demonstrated ownership of inference cost, latency, capacity, and operational considerations alongside model quality.
• Experience making build-versus-buy decisions for machine learning and AI capabilities.
• Strong ability to communicate technical findings, limitations, and trade-offs to executive audiences.
• Entrepreneurial experience is preferred, 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 large-scale entity resolution is preferred.
• Experience with web-scale language processing involving multilingual or noisy text is a plus.
• Experience working with knowledge graphs or agent memory systems is preferred.
• Experience with model post-training and distillation is advantageous.
• Familiarity with open-weight model serving is a plus.
• Knowledge of AI governance and safety practices, including ISO/IEC 42001 or the NIST AI Risk Management Framework, is preferred.

Benefits:

• Base salary range of $233,100–$366,300 USD for the United States.
• Additional compensation may include bonus, commission, equity, and other benefits, depending on the position and applicable factors.
• Comprehensive benefits designed to support employees and their families.
• Holistic mind, body, and lifestyle programs focused on overall well-being.
• Compensation may vary based on work location, qualifications, skills, experience, and training.
• Remote work arrangement.