ML Engineer

ML Model Development & Adaptation:

• Adapt and fine-tune geospatial foundation models as backbone architectures for domain-specific deep neural network heads.
• Prepare, curate, and manage training datasets from remote sensing sources and field plot inventories.
• Evaluate model performance using standard remote sensing accuracy metrics and field-based validation data.

Pipeline & Data Engineering:

• Integrate trained ML models into Vibrant Planet’s automated geospatial data pipeline as containerized, orchestrated inference services.
• Build and maintain STAC infrastructure for data discovery, cataloging, and access control of ML model inputs and outputs.
• Design and implement larger pipelines composed of many smaller DAGs (Airflow), ensuring idempotency, observability, and fault tolerance.

Knowledge Dissemination & Cross-Team Collaboration:

• Write and contribute to scientific manuscripts describing methods, validation results, and novel applications.
• Serve as a cross-team link between SciDev, Data Engineering, and Product—translating requirements, communicating constraints, and aligning priorities.
• Document pipelines, model architectures, and operational procedures in team knowledge bases.