Product Manager at Model ML

Job Title

Product Manager

Not Disclosed

Model ML is a $90M funded enterprise AI platform that recently closed a record-breaking $75M Series A backed by FT Partners, Y Combinator, and Local Globe. They are building a revolutionary AI workflow builder that automates complex manual processes for major financial institutions, transforming how global teams produce and validate client-ready work.

As a Product Manager at Model ML, you will own core product areas end-to-end, moving from customer discovery to high-impact launches. Working closely with founders and engineers, you will translate complex financial workflows into opinionated AI systems. This high-autonomy role requires a deep technical understanding to build tools for the world's leading financial institutions.

London, UK

• Join one of the fastest-growing enterprise AI platforms globally following a massive $75M Series A round backed by elite investors like Y Combinator, Local Globe, and QED.
• Report directly to founders with two successful venture-backed exits, gaining unparalleled mentorship while building a generational company from the ground up.
• Take true ownership of mission-critical product bets in a high-velocity environment where you can shape the future of AI-driven financial services.

• Own the entire product lifecycle from discovery and problem framing to scoping, design, and long-term impact measurement for core product areas.
• Spend significant time with bankers and investors to deeply understand their workflows, translating qualitative feedback into sharp, opinionated PRDs and product bets.
• Collaborate with engineering and design to rapidly prototype solutions using no-code tools and oversee the technical delivery of high-quality, reliable AI features.

• Brings 4+ years of Product Management experience, ideally within high-growth startups or building technical products for finance, data, or developer audiences.
• Demonstrates a strong product sense with the ability to navigate architectural trade-offs, data constraints, and complex AI behavior alongside engineering teams.
• Thrives in fast-paced environments with changing priorities, showing the tenacity to ship quickly and refine products based on real-world usage and data.