Company Overview:

Model ML is the AI workflow builder transforming how major financial institutions produce and validate client-ready work. Model ML converts complex, manual processes into fully automated AI systems that scale across integral teams. In under a year, Model ML has become one of the fastest growing enterprise AI platforms worldwide and recently closed a $75 million Series A, one of the largest fintech Series A rounds ever. The round was backed by FT Partners, Y Combinator, LocalGlobe, QED, 13books, and other top global investors.

Job Description:

The Engagement Manager (EM) owns the end-to-end customer outcome: translating client problems into scalable Model ML workflows, running clean operating cadence, and driving adoption through clear governance and communication.

Responsibilities (aligned to EM Framework):
- Product Quality (workflows + platform judgment): Turn client problems into scalable, measurable workflows. Balance client needs with platform constraints. Build reusable playbooks, templates, and demo assets.



Stay current on product direction and translate updates into field-ready examples.
- Organisation (systems + transparency): Maintain clean tracking across dashboards, hubs, and Notion pages. Keep usage, adoption, and health metrics accurate and up to date. Run a clear delivery plan (owners, dates, dependencies) and surface risks early.
- Communication (high-signal alignment): Prevent surprises. Send agendas before meetings, capture decisions, and publish action logs with owners and due dates. Communicate trade-offs and blockers early to both client and internal teams.
- Client Relationships (governance + ROI): Build trusted, data-driven relationships. Own trial -> conversion -> renewal motion in partnership with Growth. Drive adoption through builders, measure ROI, and turn feedback into a concrete plan with Engineering and Product.

What you can expect:
- It won't be easy; in fact, it will be very hard
- BUT, it will be a lot of fun
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📌 Engagement Manager (Madrid)
🏢 Model ML
📍 Madrid