The Role
As a Machine Learning Engineer, you'll develop and improve ML models for geospatial analytics, collaborating with various teams to enhance performance and reliability of ML systems.
Summary Generated by Built In
About the role
As a Machine Learning Engineer (m/f/x), you will work on the machine learning systems powering our geospatial analytics platform. You will contribute to the development, deployment, and improvement of production ML models that analyze satellite imagery and detect environmental risks at scale. You will collaborate closely with product managers, software engineers, and geospatial experts to build scalable ML solutions that support real-world sustainability and compliance use cases. Your work may include improving existing deforestation detection models, developing new deep learning approaches for remote sensing data, and building reliable tooling and infrastructure for model training and deployment.
As a Machine Learning Engineer (m/f/x), you will work on the machine learning systems powering our geospatial analytics platform. You will contribute to the development, deployment, and improvement of production ML models that analyze satellite imagery and detect environmental risks at scale. You will collaborate closely with product managers, software engineers, and geospatial experts to build scalable ML solutions that support real-world sustainability and compliance use cases. Your work may include improving existing deforestation detection models, developing new deep learning approaches for remote sensing data, and building reliable tooling and infrastructure for model training and deployment.
Your Responsibilities
- Develop, train, and improve machine learning models for geospatial and satellite imagery analysis
- Contribute to the full ML lifecycle, including experimentation, evaluation, deployment, monitoring, and maintenance
- Work on production systems that process large-scale satellite and geospatial datasets
- Collaborate with ML engineers, backend engineers, product teams, and geospatial analysts to deliver reliable analytics products
- Improve model performance, scalability, and robustness across different geographies and datasets
- Build and optimize data pipelines, tooling, and workflows for efficient ML development
- Apply modern deep learning and statistical techniques to remote sensing and environmental data
- Support rapid experimentation while maintaining production reliability
- Contribute to technical discussions, code reviews, and engineering best practices
You may be a good fit if you:
- Have a strong quantitative background in computer science, engineering, mathematics, remote sensing, or a related field
- Have strong programming skills in Python and hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow
- Hands-on experience with MLOps tools such as Weights & Biases, cloud infrastructure including Amazon Web Services, and/or high-performance computing environments
- Feeling comfortable developing, training, and optimizing machine learning models in production environments
- Communicate clearly and collaborate effectively across technical and non-technical teams
- Have experience working with large datasets and distributed data processing workflows
- Work effectively with AI-assisted development and coding tools
- Are fluent in English (C1+). German skills are a plus
Nice to have
- Experience with computer vision tasks such as segmentation, classification, change detection, or time-series analysis
- Experience working with remote sensing or satellite data (SAR, optical, LIDAR)
- Familiarity with geospatial data processing libraries and tools
- Experience deploying ML systems into production environments
- Understanding of environmental, climate, or sustainability-related use cases
- Experience translating complex business or regulatory requirements into data-driven solutions
Join us for this and more...
- The opportunity to work on meaningful technology with real-world environmental impact
- A highly technical and collaborative team environment
- Modern ML infrastructure and large-scale geospatial datasets
- Ownership and growth opportunities based on your experience and interests
- Flexible working environment in our Munich office near Sendlinger Tor
- Competitive compensation and benefits
About
osapiens develops holistic Software-as-a-Service solutions that help global companies ensure transparency, efficiency, and trust across their entire value chain. Through its cloud platform, the osapiens HUB, the company leverages innovative technologies, including artificial intelligence, to strengthen businesses while promoting human rights, ecological responsibility, and sustainable corporate governance.Founded in 2018 and headquartered in Mannheim, Germany, osapiens works with around 2,500 companies in over 50 countries across industries such as consumer goods, automotive, fashion, pharmaceuticals, and medical products. The company is backed by Goldman Sachs and Decarbonization Partners, reinforcing its commitment to responsible, sustainable growth and has reached unicorn status in January 2026.
Skills Required
- Strong quantitative background in computer science, engineering, mathematics, remote sensing, or related field
- Strong programming skills in Python
- Hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow
- Experience with MLOps tools and cloud infrastructure
- Experience working with large datasets and distributed data processing workflows
- Fluency in English (C1+)
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The Company
What We Do
osapiens develops holistic Software-as-a-Service (SaaS) solutions that help global companies ensure transparency, efficiency, and trust across their entire value chain. Through its cloud platform, the osapiens HUB, the company leverages innovative technologies, including artificial intelligence, to strengthen businesses while promoting human rights, ecological responsibility, and sustainable corporate governance. Headquartered in Mannheim, Germany, osapiens supports over 2,500 customers worldwide, from SMEs to global enterprises, in driving sustainable growth and regulatory compliance.
