ML Engineer

Department: Engineering

Reports To: Engineering Manager, Infra Lead

Location: Remote, US | [Pacific / Mountain time zone preferred]

Employment: Full-time

We are a team of leaders in fire science, applied science, forestry, policy, and tech who work with land managers, community risk managers, utilities, and insurers to drive action that lowers the risk of destructive wildfire. Our cloud-based, AI-driven platform modernizes land management planning, community risk assessment, and monitoring through scenario building, decision support, and treatment outcome detection.

Our fire science subsidiary, Pyrologix, produces leading wildfire science and models that quantify wildfire hazard and risk, and the benefits of mitigation action. This science powers the core Vibrant Planet platform and supports our work across utilities, insurance, and other sectors.

Vibrant Planet is backed by climate and resilience leaders including Grantham Foundation, Earthshot, Elemental Excelerator, Ecosystem Integrity Fund, Cisco, and Halogen Ventures. For more information, visit  and

Vibrant Planet () harnesses data-driven science and cloud-based technology to help make communities and ecosystems more resilient in the face of global change. Our ML Engineering team sits at the intersection of machine learning, remote sensing, and forest ecology—building the models, pipelines, and data products that power our Land Tender decision-support platform.

We are seeking a ML Engineer to build, adapt, and operationalize foundation model-based deep learning systems that estimate forest structure metrics from remotely sensed data. In this role you will fine-tune and adapt geospatial foundation models as a backbone to custom deep neural network heads, integrate trained models into Vibrant Planet’s automated production pipeline, and maintain the surrounding data infrastructure. You will also contribute to scientific knowledge dissemination through manuscripts and serve as a key cross-team link between Sci Dev and Data Engineering.

• Adapt and fine-tune custom or publicly available geospatial foundation models as backbone architectures for domain-specific deep neural network heads that estimate forest structure metrics (canopy height, biomass, basal area, etc.)
• Prepare, curate, and manage training datasets from remote sensing sources (Sentinel-2, Sentinel-1, Landsat, lidar, NAIP) and field plot inventories
• Evaluate model performance using standard remote sensing accuracy metrics and field-based validation data
• Contribute to experiment design, hyperparameter optimization, and ablation studies in coordination with the Technical Lead ML Engineer

• Integrate trained ML models into Vibrant Planet’s automated geospatial data pipeline as containerized, orchestrated inference services
• Build and maintain STAC (Spatio Temporal Asset Catalog) 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
• Maintain and improve data ingestion, preprocessing, and quality control workflows for satellite imagery and ancillary datasets
• Monitor pipeline health and model drift; implement alerting and automated retraining triggers as needed
• Develop model cards for summarization of modeling methods and performance

• Write and contribute to scientific manuscripts describing methods, validation results, and novel applications
• Serve as a cross-team link between Sci Dev, Data Engineering, and Product—translating…