Machine Learning Engineer (Geospatial AI)

TL;DR

Machine Learning Engineer (Geospatial AI): Develop, deploy, and improve production ML models analyzing satellite imagery to detect environmental risks with an accent on deep learning for remote sensing data and scalable ML infrastructure. Focus on full ML lifecycle management, model optimization for large-scale datasets, and building reliable tooling for production deployment.

Location: Hybrid in Munich office near Sendlinger Tor

Company

hirify.global develops SaaS solutions using AI for supply chain transparency, sustainability, and compliance, serving 2,500+ companies in 50+ countries; unicorn status backed by Goldman Sachs.

What you will do

• Develop, train, and improve ML models for geospatial and satellite imagery analysis
• Manage full ML lifecycle: experimentation, evaluation, deployment, monitoring, and maintenance
• Process large-scale satellite and geospatial datasets in production systems
• Collaborate with ML engineers, backend engineers, product teams, and geospatial analysts
• Optimize model performance, scalability, and robustness across geographies
• Build data pipelines, tooling, and workflows for efficient ML development

Requirements

• Fluent in English (C1+); German is a plus
• Strong quantitative background in computer science, engineering, mathematics, remote sensing, or related
• Strong Python programming and experience with PyTorch or TensorFlow
• Experience with MLOps tools (e.g., Weights & Biases), AWS, high-performance computing
• Hands-on with production ML models, large datasets, distributed processing
• Clear communication and collaboration across teams

Nice to have

• Computer vision: segmentation, classification, change detection, time-series
• Remote sensing/satellite data (SAR, optical, LIDAR)
• Geospatial data processing libraries
• Deploying ML to production
• Environmental/climate/sustainability use cases

Culture & Benefits

• Highly technical, collaborative team environment
• Modern ML infrastructure and large-scale geospatial datasets
• Ownership and growth opportunities
• Flexible working in Munich office
• Competitive compensation and benefits
• Meaningful work with real-world environmental impact