Machine Learning Engineer

Role highlights:

Machine Learning Engineer

Location: Espoo, Finland. Relocation to Finland required

Department: Product Engineering

Reports to: Geospatial Machine Learning Team Lead

Employment type: Permanent

Workplace model: Hybrid

Employment is subject to applicable security screening (incl. SUPO, where required)

Why this role matters:

As a Machine Learning Engineer, you’ll turn Earth Observation data into intelligence that helps governments, insurers, and emergency responders make faster, better decisions. You'll build and deploy ML systems that sit at the core of ICEYE's products, with direct impact on what customers can see and act on at global scale. Working in a cross-functional team alongside domain experts, product managers, and software engineers, you'll contribute across the full ML lifecycle, from data pipelines and model development through to deployment and continuous improvement.

Who We Are

ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth.

ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system.

ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future.

Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific.

Your day-to-day responsibilities

Design, develop, and deploy machine learning models and services for Earth Observation applications

Contribute to the development, fine-tuning, evaluation, and operationalization of foundation models and large-scale representation learning approaches

Build and maintain scalable ML pipelines for data preparation, training, validation, and inference

Work with large-scale Earth Observation datasets, including SAR, optical, and multi-modal sources

Collaborate with domain experts and product teams to translate business and customer needs into ML solutions

Improve model performance, reliability, and operational efficiency through rigorous evaluation and monitoring

Contribute to reusable ML infrastructure, tooling, and shared best practices within the team

Support experimentation and rapid prototyping for new product