Machine Learning Engineer
RemoteFinland or Espoo, Uusimaa, Finland
Job Summary
Design, develop, and deploy machine learning models and services for Earth Observation applications, including foundation models and large-scale representation learning. Build and maintain scalable ML pipelines for data preparation, training, validation, and inference using datasets such as SAR, optical, and multi-modal sources. Collaborate with domain experts and product teams to translate business needs into ML solutions while improving model performance through rigorous evaluation and monitoring. Maintain existing production systems and contribute to reusable ML infrastructure and shared best practices. Support experimentation and rapid prototyping for new products while integrating ML systems into broader engineering and customer-facing solutions.
Required Qualifications
- MSc or PhD in Computer Science, Machine Learning, Remote Sensing, Data Science, or a related field, or equivalent practical experience
- Solid experience developing machine learning models using modern frameworks such as PyTorch or TensorFlow
- Experience with foundation models, self-supervised learning, representation learning, or large-scale deep learning systems
- Experience deploying and maintaining machine learning models in production environments
- Strong software engineering skills in Python and familiarity with modern software development practices
- Solid understanding of model evaluation, validation, reproducibility, and performance monitoring
- Experience building reliable data and ML pipelines
- Relocation to Finland required
- Employment is subject to applicable security screening (incl. SUPO, where required)
Desired Qualifications
- Experience with Earth Observation data (SAR, optical, or multi-modal)
- Familiarity with foundation models for Earth Observation or geospatial applications
- Experience with MLOps, CI/CD, model registries, and cloud-based ML platforms
- Knowledge of geospatial data formats and large-scale data processing frameworks
- Experience with distributed training and large-scale model optimization
- Experience with near real-time or operational monitoring systems
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