Machine Learning Engineer - Geospatial AI

• Design, build and maintain ML/AI models from diverse geospatial data for tracking sustainable agriculture.
• Preprocess and analyze large, complex geographic datasets (e.g., satellite imagery, sensor/field data), making them ready to be used with ML techniques to identify patterns and trends.
• Enhance Application Readiness of ML/DL models to support dMRV platform and carbon science.

This role creates positive change by developing ML/AI models for tracking sustainable agriculture and supporting carbon science through geospatial data analysis, contributing to environmental sustainability and climate solutions.

• 5+ years experience in ML Engineering / Applications including productionization of geospatial agriculture models
• Experience across diverse AI/ML architectures including design, implementation, evaluation, & deployment
• Expert in Python & geospatial data handling with knowledge of common remote sensing assets like Sentinel, Landsat, Planet, soils, weather
• Experience with statistical evaluations, assessing model performance and behavior, benchmarking, interpreting accuracy metrics, and automated model testing frameworks
• Proficiency with libraries and frameworks such as PyTorch, VertexAI, TensorFlow, Keras, Scikit-learn, Airflow, Docker, etc. Some experience with higher order tools like Geo-Foundational, CNNs, Transformers, LSTMs, ResNets, Embeddings, GANs, LLMs
• Comfortable working with agile data scientists, high-growth environments with rapid iteration and evolving priorities, cross functional teams, version control, and ticketing

No benefits, compensation, or perks mentioned in the job description.

No information provided about the organization's mission and work in the job description.