Director of AI Engineering

### Who you are
- The ideal candidate will have experience growing and managing multiple AI engineering teams and managers within a product company, possessing a proven ability to align AI agentic infrastructure with concrete business outcomes, and anchor the organization's technical work within our broader corporate strategy
- Education: MS/Ph.D. in Computer Science, Artificial Intelligence, Computational Linguistics, or a similar quantitative discipline required
- Leadership: 5+ years of experience in ML or AI engineering leadership roles, specifically managing multiple teams (including managing managers) shipping production software
- Technical Expertise: Deep system-level architectural knowledge of modern NLP, large language models (LLMs), transformer architectures, RAG pipelines, and agentic workflows (planning, reasoning, tool use, human-in-the-loop workflows)
- Advanced Evaluation Skills: Deep knowledge of evaluation methodologies for non-deterministic systems, including LLM-as-a-Judge, behavioral analysis (evaluating reasoning traces or chain of thought), and championing best practices in model lifecycle management and monitoring
- Data Systems: Expertise in vector databases, and SQL; experience with handling unstructured text data at scale to build foundational data architectures
- Technical Mastery: Deep architectural understanding and proven experience designing and overseeing the core components of enterprise AI platforms. You must bring specific architectural expertise in scalable Model Hosting and Training, managing enterprise-wide Knowledge Graphs, building robust Agent Hosting environments, implementing Reinforcement Learning pipelines, and designing a comprehensive Intelligence Store, You should act as an "Engineer of Engineers," capable of evaluating complex technical tradeoffs across these specific components to guide your teams toward highly scalable, efficient, and secure solutions
- Communication: Great communication skills, including the ability to communicate complex AI concepts (like hallucinations or context windows), as well as AI capabilities, limitations, and risks to business stakeholders and articulate product vision to audiences ranging from individual contributors to C-level executives
- Cross-Functional Collaboration: Proven ability to act as a bridge between Engineering, Product Management, and executive leadership, anticipating future business needs and translating competitive market offerings into scalable technical insights
- Adaptability: Strong judgment in navigating ambiguity and guiding engineering teams in the fast-paced Generative AI landscape, balancing rapid innovation in AI with long-term platform stability and maintainability

### What the job involves
- The AI engineering organization at ServiceTitan is looking for a leader who will drive the core infrastructure of our AI strategy to support our growth now and in the future
- They will work with business stakeholders across the company and support the creation of a global AI Agentic Infrastructure competency that includes intelligent, autonomous, and semi-autonomous systems
- Set the engineering vision and direct the architecture of ServiceTitan's core AI infrastructure and Multi-Agent Systems encompassing model hosting/training, agent hosting, reinforcement learning, intelligence store and rigorous evaluation that bridges agent specifications to business outcomes
- Partner with Product leaders to co-create vision, set strategic direction, and define the technical roadmap for foundational AI capabilities, ensuring the infrastructure seamlessly scales to support all platform-level agents and future product innovations, while ensuring agentic systems are designed with human-in-the-loop controls, observability, and auditability
- Define enterprise-wide standards, budgets, and governance for LLM capabilities and trade-offs (latency, cost) to drive superior customer experiences at scale, and evaluate different technologies to make informed platform-level decisions aligned with business goals
- Oversee highly available data processing architectures, Enterprise-wide Knowledge Graphs, and the foundational Intelligence Store, and feedback loops essential for successful RAG and agentic pipelines at a domain-wide scale
- Architect and drive the adoption of scalable LLMOps and AgentOps frameworks, including building a centralized, high-throughput "Evaluation Core" platform for supporting, evaluating, and securing complex multi-agent deployments in parallel with agent execution
- Direct the architectural integration of advanced techniques (multi-agent systems, function calling, fine-tuning) to enhance agent capabilities and drive operational excellence across the organization
- Align with Engineering and Product leadership on roadmaps to ensure timely delivery of robust production agents that drive revenue and growth, while managing real-world constraints like token budgets, inference latency, and cost-normalized accuracy at an enterprise scale
- Implement governance for ethical, explainable, and compliant AI usage, including data handling and bias considerations
- Hire, mentor, and retain a high-performing AI Engineering organization, including developing and coaching engineering managers, fostering a culture of ownership, accountability, experimentation, and responsible AI development

### Benefits
- Flexible time off
- Fully paid individual medical, dental, and vision coverage
- Access to on-demand services like Ginger, Headspace, One Medical, Galileo, Aaptiv, and more
- Tax free savings via HSA, FSA, and Dependent Care FSA
- 401(k) match
- Personal development programs
- Medical plan with up to $20,000 in fertility coverage
- Up to 30 weeks of paid leave for birthing parents and up to 16 for non-birthing parents
- Adoption support including reimbursement of up to $20,000
- Bereavement leave for loss of a pregnancy or loved one
- Access to programs for new parents including Maven Maternity, Ready, Set, Food! Wellthy, SNOO Smart Sleeper, and more