What you'll do:
- Contribute to the design, development, and maintenance of end‑to‑end ML pipelines.
- Work with image data and computer vision models.
- Build, curate, and maintain ground‑truth catalogs and labeling workflows, ensuring high‑quality datasets for training and evaluation.
- Implement model monitoring, validation checks, and drift/anomaly detection to ensure reliability and consistency across environments.
- Monitor ML systems and pipelines, triaging issues, and collaborating with senior engineers and data scientists to identify and resolve root causes.
- Support performance optimization for both training and inference pipelines, including GPU utilization and batch/image processing workloads.
- Participate in managing cloud‑based ML environments (Azure ML, Databricks, or similar platforms), ensuring reproducibility and scalability.
- Use orchestration tools to schedule and maintain recurring ML workflows, such as model retraining, batch inference, or feature refresh processes.
- Assist in building and maintaining metadata tracking, feature stores,



and access controls for ML assets.
- Contribute to dashboards and internal tools for data quality, model performance monitoring, and system observability.
- Collaborate to understand requirements and support ML and CV initiatives within the organization.
- Document pipelines, processes, and ML best practices to improve team knowledge sharing and onboarding.
- Work in a versátil, mostly remote environment that supports work-life balance.

What you'll need:
- 2+ years of experience as a ML Engineer or similar role.
- Degree in Computer Science, Mathematics, or related field (Bachelor’s/Master’s/PhD).
- Proficiency in Python, Spark.
- Familiarity with MLOps practices and production-grade ML environments.
- Direct experience with model deployment, MLflow, feature stores, or model registries.
- Basic knowledge of orchestration and containerization tools (Airflow, databricks, Docker)
- Experience working with cloud

📌 Machine Learning Engineer (Madrid)
🏢 TomTom
📍 Madrid