Machine Learning Engineer
TomTom
full-time
Posted on:
Location Type: Hybrid
Location: Madrid • Spain
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About the role
- 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.
Requirements
- 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 platforms (Azure, AWS, or GCP).
- Understanding of ML workflows, including feature engineering, model training, experiment tracking, and deployment.
- Familiarity with Git and CI/CD workflows.
- Good communication skills and a proactive, problem-solving mindset.
Benefits
- A competitive compensation package.
- Time and resources to grow and develop, including a personal development budget and paid leave for learning days.
- Paid access to e-learning resources such as O’Reilly and LinkedIn Learning.
- Enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities.
- Work flexibility, where TomTom’ers, in agreement with their manager and team, use both the office and home.
- Improve your home office with a setup budget and get extra support with a monthly allowance.
- Enjoy options to work from your home country and abroad for a set number of days each year.
- Take the holidays you want with a competitive holiday plan, plus an extra day off to celebrate your birthday.
- Join annual events like our Hackathon and DevDays to bring your ideas to life.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningcomputer visionPythonSparkMLOpsmodel deploymentfeature engineeringexperiment trackingmodel trainingmodel monitoring
Soft Skills
communicationproblem-solvingcollaborationproactive mindset
Certifications
Bachelor's degreeMaster's degreePhD