Data Engineer

We're looking for a Data Engineer & MLOps Engineer to own and scale the data and ML infrastructure behind our platform.

This is not a maintenance role - you'll be building systems from scratch, making key architectural decisions, and working directly on production AI pipelines connected to real-world environments (kitchens, cameras, edge devices).

You will be responsible for everything that happens between raw data and reliable AI in production.

What you´ll do

• Design and build end-to-end data pipelines (from edge devices to cloud)
• Own the infrastructure that powers our computer vision systems in production
• Deploy, version, and monitor machine learning models at scale
• Build robust MLOps workflows (training * evaluation * deployment * monitoring)
• Ensure data quality, reliability, and observability across the platform
• Optimize pipelines for performance, scalability, and cost
• Work with large-scale image data and real-time ingestion systems
• Support the integration and improvement of machine learning and computer vision models (data preparation, evaluation, and iteration loops)
• Contribute to improving model performance in production through better data, monitoring, and feedback pipelines
• Make foundational decisions on architecture, tooling, and infrastructure

Do you have experience in Data science?, Do you have a Old bachelor's degree?, We're looking for someone with around 3+ years of experience in data engineering, MLOps, or related roles, comfortable working in early-stage environments and taking ownership end-to-end., * Strong experience with Python and data-intensive systems

• Experience building and maintaining production data pipelines
• Solid understanding of cloud infrastructure (GCP preferred, AWS also valid)
• Hands-on experience with Docker and production deployments
• Familiarity with MLOps concepts (model lifecycle, monitoring, reproducibility)
• Experience with workflow orchestration tools (Airflow, Prefect, or similar)
• Strong engineering mindset: you care about reliability, scalability, and clean systems
• Comfortable working in ambiguity and taking ownership of problems end-to-end

Strong Plus

• Experience deploying ML models in production
• Experience with computer vision pipelines
• Familiarity with Kubernetes or similar orchestration systems
• Experience with tools like MLflow, Weights & Biases, or feature stores
• Experience working with streaming or near real-time data systems

What makes this role differente?

• You'll work on real AI systems in production, not experiments
• Your work will directly impact how much food is wasted every day
• You'll have high ownership over critical infrastructure from early stage
• You'll help define how our data and ML platform is built from scratch
• You'll be part of a small, high-impact team, where things move fast and ship often

Pulled from the full job description

• Flextime
• Professional development assistance
• Employee stock ownership plan
• Work from home
• Opportunities for advancement

Gambooza is a growing AI startup based in Madrid, building computer vision systems to help restaurants reduce food waste, tackle operational inefficiencies, and improve their long-term viability. We're tackling a massive, overlooked problem: inefficiencies and food waste in food service. Our technology brings visibility into kitchen operations using AI, helping operators reduce costs and environmental impact. We're an early-stage company, already backed by top programs like Lanzadera, Madrid Food Innovation Hub, Basque Culinary Center, and EU Tech Funds, and recognised in competitions such as the Future Gastronomy Startup Competition and Premio Emprendimiento Digital (Comunidad de Madrid). We're now entering a scaling phase, moving from pilots to real deployments - and building the infrastructure to support it. You will join as a key early member of the tech team, working closely with the founders and acting as the second core technical profile, with ownership over data and ML infrastructure.