Director of Machine Learning & Artificial Intelligence

Accountabilities • Technical strategy and innovation: Define and evolve the Machine Learning and AI strategy in partnership with senior technical and product leaders, assessing emerging technologies, research, customer needs, and opportunities to determine where to invest, build, partner, or experiment.

• Innovation roadmap: Lead exploration, prototyping, validation, and de-risking of new ML and AI capabilities that can strengthen products, technical infrastructure, and long-term strategy.

• Responsible AI and governance: Ensure ML systems and practices meet expectations for privacy, security, responsible AI, regulatory compliance, reliability, and operational excellence, including appropriate oversight of sensitive datasets and external data partners.

• Team leadership: Develop and expand a high-performing ML organization by recruiting, retaining, coaching, and empowering exceptional technical talent and developing future technical leaders.

• Culture and performance: Establish clear expectations, provide constructive feedback, encourage psychological safety and healthy technical debate, and foster a culture of accountability, continuous learning, inclusion, and technical excellence.

• Cross-functional collaboration: Build strong partnerships across ML, Engineering, Product, Research, Technical Program Management, and other teams to connect research, experimentation, product development, and production delivery.

• Production execution: Partner with engineering leaders to ensure ML capabilities are scalable, reliable, secure, maintainable, and production-ready, with clear technical ownership and operating practices.

• Organizational leadership: Establish priorities, operating rhythms, capacity plans, and resource allocation approaches that balance innovation with delivery, quality, cost, reliability, security, and long-term sustainability.

• Strategic decision-making: Identify organizational and technical constraints, clarify tradeoffs, resolve competing priorities, and contribute to broader technology investment and planning decisions.

• External representation: Represent the organization’s ML and AI work with customers, strategic partners, funders, conferences, advisory groups, and the wider technical community when appropriate.

Requirements

• Leadership experience: Significant experience leading Machine Learning, AI, Data Science, or related technical teams responsible for production capabilities at meaningful scale.

• Technical expertise: Strong technical judgment across modern ML and AI, including the ability to evaluate architectural and technical tradeoffs and translate emerging research into practical capabilities.

• Relevant domain knowledge: Deep understanding of AI/ML applications, content detection technologies, and software development, with experience developing and operating production ML systems.

• Responsible technology: Experience incorporating reliability, evaluation, monitoring, privacy, security, governance, and responsible AI principles into ML development and operations.

• People management: Demonstrated ability to recruit, develop, retain, and empower high-performing technical teams, including coaching senior technical leaders and individual contributors.

• Organizational leadership: Ability to establish ownership, delegate effectively, set clear expectations, provide candid feedback, and build leadership capacity across a team.

• Cross-functional influence: Proven experience collaborating with Engineering, Product, Research, and other stakeholders while creating alignment across complex technical and organizational environments.

• Strategic communication: Strong communication and presentation skills, including the ability to explain complex technical concepts clearly, ask critical questions, influence decisions, and represent technical work externally.

• Problem-solving: Ability to influence change, navigate ambiguity, make sound tradeoffs, and address complex problems calmly and strategically.

• Leadership approach: Empathy, humility, transparency, honesty, clarity, accountability, and a trauma-informed approach to leadership and collaboration.

• Mission alignment: Commitment to keeping the needs and safety of children at the center of technical and organizational decisions.

Benefits

• Salary: Estimated annual compensation range of $188,000–$302,450 , with actual compensation determined by factors such as location, experience, skills, training, licenses, and certifications.

• Remote-first work: Work primarily from home within a remote-first operating model.

• Travel opportunities: Periodic travel may be required for organization-wide gatherings, team meetings, team-building events, conferences, or other business needs.

• Professional growth: Opportunities to develop technical leadership capabilities while shaping a growing ML and AI function.

• Collaborative environment: Work alongside multidisciplinary teams spanning technology, data, product, research, and business functions.

• Inclusive culture: A workplace that values diverse professional backgrounds, expertise, cultures, and perspectives.

• Mission-driven work: The opportunity to apply advanced technology to complex challenges with meaningful social impact.

• Employee support: A broad benefits offering designed to support employees’ professional and personal well-being.

• Accessibility: Reasonable accommodations are available to qualified candidates and employees with disabilities.

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