About the Client 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 a Member of Technical Staff, Machine Learning, you'll build core ML components and work on real production systems from day one
- learning how large-scale ML behaves outside of research settings. This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML.
What You'll Work On Build and improve ML components across data, training, evaluation, and inferenceFine-tune and adapt models as part of larger production systemsImplement evaluation and testing to understand model behaviorHelp build and maintain data pipelines for real-world and synthetic dataDebug model issues, performance problems, and production incidentsShip improvements iteratively and learn from real user feedbackWork closely with senior ML engineers and product teamsWork under real production constraints: latency,



cost, reliability, and safetyRequirements Strong foundations in machine learning and modern neural architecturesSome hands-on experience training, fine-tuning, or deploying ML modelsComfortable writing production-quality code and learning new tools quicklyCurious, coachable, and eager to learn from real production systemsAble to work through ambiguity with guidance, growing ownership over timeA bias toward shipping, iteration, and continuous improvementTech Stack Python
- PyTorch / JAX
- production ML systems running on GPUsWhat Success Looks Like ML models in production meet accuracy, latency, and reliability targetsProduction issues are found quickly, debugged effectively, and root-causedData pipelines, training loops, and inference systems stay robust, reproducible, maintainableEffective collaboration with engineers, product, and research to ship reliable ML-powered featuresIteration is driven by real-world signals and measurable improvementWhat 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.

📌 Member Of Technical Staff, Machine Learning (Madrid)
🏢 DNA325
📍 Madrid