Senior Manager, AI

About the Role: We are building the first B2B, in-classroom Voice + Video AI Tutor designed for real-world learning environments. Our system operates in live classrooms—processing noisy audio, interpreting visual signals, and delivering low-latency, safe, pedagogically aligned AI support to educators and students. We are seeking a Senior AI / Machine Learning Manager to lead a team of 3–5 ML engineers and data scientists building multimodal AI systems that power critical product features across our platform. This team develops** trained models, generative AI systems, and agentic learning assistants that support millions of learners and educators. You will be responsible for defining the technical direction, mentoring the team, and delivering production-ready AI capabilities that operate reliably in complex real-world environments. This is a high-impact role with significant technical ownership,** shaping how AI is embedded into our platform—from early research and experimentation through deployment and continuous optimization. You will work closely with Product, Data, Platform, and Engineering leaders to bring cutting-edge AI capabilities into classrooms at scale.

Technical Leadership: • Lead the design, development, and deployment of AI-powered product features, including trained models, generative AI systems, and agentic learning assistants. • Architect systems that support real-time multimodal AI**,** integrating signals from audio, video, and text to support classroom interactions. • Guide architecture decisions for LLM orchestration, prompt frameworks, retrieval systems, and agent-based workflows. • Ensure AI systems meet strict requirements for latency, safety, and reliability in live classroom environments. • Establish best practices for model development, evaluation, monitoring, and MLOps.

Agentic Learning Systems: • Design and implement agentic AI systems that guide, coach, and teach students through interactive learning experiences. • Build AI capabilities that support adaptive instruction, classroom assistance, and educator workflows. • Develop safe, pedagogically aligned AI interactions appropriate for real-world classroom use.

AI Quality, Evaluation & Experimentation: • Establish rigorous evaluation frameworks for generative AI and ML systems. • Implement both automated evaluation pipelines and human-in-the-loop review processes. • Monitor system performance and continuously improve models through experimentation and feedback loops.

Platform & Infrastructure: • Collaborate with platform and DevOps teams to build scalable AI infrastructure and services. • Develop reusable tooling for model training, deployment, monitoring, and experimentation. • Ensure AI systems are cost-efficient, observable, and maintainable at scale.