Manager, Applied AI - Title Launch Management

Guide the team in designing feedback loops and data infrastructure that continuously bring high-quality real-user data into evaluation and training pipelines to improve performance over time. 3+ years of direct management experience shipping production-grade AI/ML software with measurable business impact, building and managing high-performing, inclusive teams, attracting top talent, fostering accountability, and ensuring all voices inform decisions. Proficiency in software engineering fundamentals and LLMs, RAG, and agentic architectures Strong track record of designing and implementing evaluation pipelines for AI/ML products. Experience building safety guardrails to manage business risk and feedback loops for continuous improvement of models Familiarity with ML evaluation methodologies (e.g., offline metrics, online experiments, human evaluation) and integrating them into continuous improvement processes. Experience introducing automation into an established operational workflow earning operator trust, designing the human-in-the-loop handoff, and managing the change with the teams whose work is being automated. Cultivate open communication, empowering team members to give feedback and recognize achievements and growth areas. Strong product mindset; can translate high-level goals into clear team priorities and plans. Skilled at prioritizing and aligning team efforts with business objectives, able to adjust direction based on impact and new information. Effective collaborator who builds productive cross‑functional partnerships with product and engineering. Uses data, feedback, and small experiments to reduce ambiguity and make sound decisions. Communicates clearly with their team, stakeholders, and global partners. Experience in the media technology domain (e.g. content processing, digital asset workflows, creative tools) Technical proficiency with hands-on experience across the AI/ML lifecycle—ranging from traditional frameworks and MLOps platforms (e.g., PyTorch, TensorFlow, Metaflow) to modern LLM orchestration, agentic frameworks (e.g., LangGraph, DSPy, Agent SDK), and vector databases.