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 continuous improvement

Tech Stack

Python · PyTorch / JAX · LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.) · inference/serving (e.g. vLLM) · vector DB

What Success Looks Like

- ML models in production meet accuracy, latency, and reliability targets
- Production issues are found quickly, debugged effectively, and root-caused
- Data pipelines, training loops, and inference systems stay robust, reproducible, maintainable
- Effective collaboration with engineers, product, and research to ship reliable ML-powered features
- Iteration is driven by real-world signals and measurable improvement

What to Expect The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently.

Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process.

If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision. To be considered, please make sure to answer the screening questions in the application form — we're not able to review submissions that skip them.

📌 Applied AI Engineer (Madrid)
🏢 DNA325
📍 Madrid