Senior Data Engineer

MITO AI Location: Madrid (hybrid)

About MITO AI MITO AI is a collaborative, AI-native platform reinventing how films, commercials, and music videos are made. We are building the operating system for a $300B+ global video production industry as it shifts to AI-native workflows.

Headquartered between San Francisco (team of 2) and Madrid (team of 14), MITO combines state-of-the-art

AI models for image, video, and audio with professional-grade editing tools in a multiplayer, browser-based canvas. Creators and teams can also bring ideas to life by directing an AI agent that generates scripts, scenes and edits in real time.

MITO was founded by

(Master MIT, PhD Cambridge, 5x founder and CEO, founded social photo startup Pixable acquired by SingTel, founded of CoverWallet $300M exit in 4 years, founded AI infrastructure company iPronics, which has raised $50M) &

(award-winning product designer at 2 tech unicorns, National Design Award recipient) and Arantxa Barcia (Art Director).

We are backed by Lightspeed Venture Partners and investors including Kibo, Kfund, Sequoia and a16z scouts, LifeX, Everywhere, 5 unicorn founders, and execs from Github and Roblox.

Role Overview This role owns the data backbone that makes that possible. You will design and scale the event, asset, and metadata architecture that powers creative collaboration, AI orchestration, and real-time editing. This is not generic CRUD backend work — this is building the foundation of a next-generation creative system.

What You Will Own

Data Architecture Design ingestion and processing pipelines for user events, AI outputs, asset metadata, and collaboration signals Architect event-driven systems that support real-time canvas interactions and timeline edits Define schemas for scenes, assets, storyboards, prompts, model outputs, and versioning Ensure traceability across multi-model AI workflows

Infrastructure & Performance Build scalable pipelines for high-volume creative assets (images, video, audio, embeddings) Optimize storage, indexing, and retrieval across structured and unstructured data Maintain performance across collaborative sessions and multi-user editing Balance cost efficiency with low latency

Observability & Reliability Implement monitoring and logging across AI calls, orchestration layers, and asset pipelines Ensure reproducibility and lineage of generated outputs Design systems that tolerate model variability and partial failures

Collaboration Work closely with AI engineers to expose structured data for orchestration and retrieval Partner with frontend engineers to optimize real-time synchronization Support product in defining metrics across activation, engagement, and creative output quality

Technical Stack TypeScript • Node.js • Python Postgres • ClickHouse • Object Storage (S3-compatible) Kafka or event streaming systems Vector databases Cloud-native deployment (Docker, CI/CD)

Experience with media processing pipelines is a strong plus.

Who We Are Looking For 5+ years building distributed backend or data infrastructure Experience with event-driven architectures and high-throughput systems Strong schema design and data modeling intuition Comfortable working with unstructured and semi-structured data Startup mindset: pragmatic, fast, accountable Curious about AI-native systems and creative workflows

What Success Looks Like Creative workflows feel instant and reliable AI outputs are traceable and reproducible Collaboration works in real time without breaking Data architecture scales as usage grows Engineering moves faster because the foundations are solid