VP, AI

About Downtown

Downtown Music Holdings (Downtown) is a modern global music company. We help millions of creators, rights holders and their partners to manage their music as a business and get paid fairly for their work. We’re working to build a more equitable global music industry because we believe when creators and their partners are fairly compensated, they are empowered to keep creating and investing back into the world.

We service over 50 million music assets from hundreds of countries across six continents, including some of the most well-known songs in popular music. Working with creators at every stage of their career, from emerging songwriters to iconic performers, our business model drives equity across the entire global music industry supply chain.

Role Overview

Downtown Music is hiring a VP of AI to guide the practical adoption of AI across our product development, engineering workflows, and business operations.

This is a hands-on, highly collaborative role. Instead of running an isolated research lab, you will partner with teams across the organization to do three things: evolve how our product teams prototype and build, support our engineering teams in adopting AI-assisted workflows, and oversee the development of practical ML models using our extensive music catalog and data.

You will need to lead through influence, helping different business units transition to modern, AI-supported processes without disrupting daily operations.

Key Responsibilities

1. AI-Assisted Engineering & SDLC

• Modernize Engineering Workflows: Lead the rollout of AI coding assistants, automated testing, and development tools to improve day-to-day engineering speed and quality.
• Upskill Teams: Create practical guidelines, training, and playbooks to help our engineering teams adopt AI-assisted development practices.
• Build Shared Infrastructure: Develop reusable, centralised AI components (like API gateways, retrieval architectures, and evaluation tools) so teams don't have to build them from scratch.

2. AI Product Innovation & Automation

• Simplify Product Prototyping: Help product managers and designers use AI tools to quickly mock up, test, and validate ideas.
• Integrate AI Features: Partner with product leaders to build reliable AI capabilities directly into our core products (such as royalty processing, publishing, and distribution).
• Workplace Automation: Collaborate with operational teams to identify and automate repetitive, data-heavy manual processes to improve speed and reduce errors.

3. AI/ML Model Development

• Utilise Our Data Asset: Work with Data Engineering and Data Science teams to build and deploy proprietary AI/ML models for use within our products. Focus on implementation of models and approaches that deliver value to our customers.
• Governance & Standards: Set clear standards for model quality, data privacy, and intellectual property compliance in partnership with our Security, Legal, and IT teams.

4. Collaboration & Influence

• Build Buy-in: Work across a decentralised, multi-brand company to show the practical value of AI and help teams manage the change.
• Stay Informed: Keep up with practical developments in AI and music tech, serving as a reliable advisor on what is useful and what is hype.

What Success Looks Like (First 12–18 Months)

• Team Adoption: Our engineering teams are comfortably using AI-assisted development tools in their daily work.
• Practical Models: A few key proprietary models are running in production, delivering clear value in rights management or analytics.
• Faster Prototyping: Product teams can quickly build and test feature concepts using AI-driven workflows.
• Drive Innovation: In a fast changing AI driven world, you will have a strong hand in driving innovation across the business, enabling teams solve new and significant problems for our clients.
• Clear Governance: We have a straightforward framework in place for safe, compliant AI usage across the company.

Candidate Profile

Required Experience

• Hands-on AI/ML Leadership: Experience building and deploying practical ML models in a production environment (not just calling third-party APIs).
• Improving Engineering Workflows: Experience rolling out modern developer tooling (such as Copilot or similar assistants) across engineering teams.
• Collaborative Change Management: A track record of helping teams adopt new technologies and processes through influence and support rather than top-down mandates.
• Pragmatic Mindset: Highly focused on shipping functional software and solving real problems, with a realistic view of AI's current limitations.

Preferred Experience

• Background in data-heavy environments (e.g., streaming services, fintech, digital rights, or royalty administration).
• Experience working within multi-brand or matrixed organisational structures.