Full Job Description
Apple Music is looking for a Music Content Operations AIML Program Lead to help drive our strategic initiative to scale music content operations through models, agents, and automation. You'll lead the transition of music curation from a primarily manual operation toward a hybrid system where agents handle day-to-day work and human expertise is focused where it matters most - high-priority, high-risk, and quality-critical decisions. This is a full-time individual contributor role on our Content Operations team, reporting into Music Content Operations leadership.
Sitting at the intersection of operations and advanced technology, you'll serve as the technical liaison between our operational workflows and the data science and engineering teams building the models, tools, and agentic systems that scale this work far beyond what manual review can achieve alone. You'll partner with engineering to develop agents, help establish the key infrastructure to support and integrate them, and define how humans stay in the loop as that infrastructure matures. You'll translate messy operational reality - everything from track-level metadata quirks to editorial judgment calls - into crisp technical requirements, evaluate the solutions that come back, and decide what ships.
Description
Own the strategy and execution plan to scale curation through models, agents, and automation - sequencing workstreams by impact and driving them from concept to production.
Partner with Engineering and Data Science to develop agents that take on day-to-day curation work, from defining requirements through evaluation and production rollout.
Help establish the key infrastructure to support, integrate, and orchestrate agents over time - shaping how agentic systems plug into existing curation workflows and data.
Define the future operating model for curation - determining where agents handle work autonomously and where humans stay in the loop for high-priority, high-risk, or quality-assessment cases, including review of agent output.
Translate operational challenges into clear, actionable specifications that engineering and data science teams can execute against.
Break down ambiguous problems into well-scoped, prioritized workstreams, sequenced by impact. Start with the highest-leverage slice; iterate from there.
Evaluate technical proposals and model outputs - participate in design reviews and build eval frameworks (golden sets, quality metrics, error analysis, human-in-the-loop feedback) to judge whether AI/ML and agentic solutions are ready for production.
Design and iterate on human-in-the-loop tooling that surfaces agent and model outputs (confidence scores, candidate matches, suggested labels, risk signals) so reviewers can validate high-stakes decisions with speed and accuracy.
Run ROI and impact analyses to distinguish short-term tactical wins from long-term structural solutions, and to prioritize where automation delivers the most leverage.
Communicate upward - build clear, compelling narratives and decks, and present progress, tradeoffs, and recommendations to senior leadership.
Partner cross-functionally with Engineering, Data Science, Product, Editorial, Analytics, and peer Operations teams (including Artist Curation, Shazam Operations, Partner Operations, and Data Strategy) to surface interdependencies and align priorities.
Minimum Qualifications
5+ years in operations, program management, or technical product roles with meaningful exposure to AI/ML-driven workflows.
Demonstrated experience translating operational or product problems into ML requirements and working directly with data scientists and engineers through model development cycles.
Exposure to LLM-based workflows, prompt evaluation, or agentic automation - particularly applied to content moderation, classification, or operational workflows.
Conceptual familiarity with content classification and content-risk tradeoffs, sufficient to judge where human review is warranted.
Demonstrated track record of managing multiple concurrent workstreams with consistent on-time delivery and proactive stakeholder communication.
Experience evaluating model outputs - designing eval sets, quality metrics, error analysis, and human feedback loops.
Experience designing or specifying review/annotation tooling that surfaces AI inputs to human reviewers.
Conceptual understanding of core analytics principles (sampling, hypothesis testing, statistical measurement).
Proven ability to produce executive-ready written narratives and presentations.
Track record of leading ambiguous initiatives end-to-end with minimal direction.
Preferred Qualifications
Experience in the music industry or with music metadata, catalogs, rights data, genre taxonomies, or editorial/streaming products.
Hands-on experience building or deploying agentic systems in production.
Experience collaborating with international stakeholders.
Comfort querying data (SQL or similar) to investigate catalog issues independently.
Tableau or comparable BI tooling.
A genuine passion for music and curiosity about the systems that power the modern music industry.
Bachelor's degree in Engineering, Business/Economics, or equivalent experience.
