About the Company
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
As an Applied AI Engineer, you'll turn model capability into real product behavior. You'll own problems end-to-end — from shaping model behavior, to building the systems around it, to making sure it holds up in production. This role sits at the intersection of machine learning, systems, and product: making AI actually work for users, not just in demos.
What You'll Work On
- Build and ship AI features end-to-end (model → system → user experience)
- Design and iterate on prompts, tools, memory, and agent workflows
- Turn raw model outputs into structured, reliable, predictable behavior
- Debug issues across the full stack — model, orchestration, infra, UX
- Optimize for latency, cost, and production reliability
- Develop lightweight evaluation frameworks to measure real-world performance
- Work closely with product and engineering to turn ambiguous problems into working systems
Requirements
- Strong foundation in machine learning and modern neural network architectures
- Hands-on experience training, fine-tuning, or deploying ML models
- Ability to write clean, production-quality code
- Comfort working across abstraction layers — model, infra, product
- Strong problem-solving skills in ambiguous, fast-moving environments
- A bias toward shipping, iteration, and
📌 Applied AI Engineer (Madrid)
🏢 DNA325
📍 Madrid