ML Engineer

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Vibrant Planet

WebsiteLinkedInAll Job Openings

11 - 50 employees

🌾 Agriculture

💰 $17M Seed Round on 2022-06

Agriculture

Vibrant Planet is a company focused on restoring natural systems through adaptive planning and market incentives. It specializes in land management, natural resource management, and climate technology, offering software that aids in scenario planning, monitoring, and reporting for natural resource managers. By prioritizing wildland resilience, Vibrant Planet helps in calculating the avoided loss and ecosystem service value of management and wildfire protection projects, thus facilitating conservation finance efforts.

📋 Description

• Build, adapt, and operationalize foundation model-based deep learning systems that estimate forest structure metrics from remotely sensed data • Fine-tune and adapt geospatial foundation models as backbones for custom deep neural network heads • Integrate trained models into Vibrant Planet’s automated production pipeline • Maintain surrounding data infrastructure • Prepare, curate, and manage training datasets from satellite, lidar, and field plot sources • Evaluate model performance using remote sensing accuracy metrics and field-based validation data • Contribute to experiment design, hyperparameter optimization, and ablation studies • Integrate ML models into automated geospatial pipelines as containerized, orchestrated inference services • Build and maintain STAC infrastructure for model input and output discovery, cataloging, and access control • Design and implement Airflow DAG pipelines with idempotency, observability, and fault tolerance • Maintain data ingestion, preprocessing, and quality-control workflows • Monitor pipeline health and model drift; implement alerting and automated retraining triggers • Develop model cards summarizing modeling methods and performance • Write and contribute to scientific manuscripts • Connect SciDev, Data Engineering, and Product by translating requirements and aligning priorities • Document pipelines, architectures, and operational procedures • Participate in code reviews, architectural discussions, and sprint planning • Follow information security, secure development, change management, and customer-data protection procedures

🎯 Requirements

• M.S. in Computer Science, Machine Learning, Remote Sensing, Data Science, Ecology, or a related quantitative field, or equivalent work experience • 3+ years of experience developing, training, and deploying deep learning models; PyTorch preferred • Strong Python proficiency, including NumPy, pandas, xarray, and scikit-learn • 3+ years of experience with geospatial data processing using rasterio, GDAL, geopandas, and shapely • Experience building and maintaining data pipelines with Airflow, Prefect, Dagster, or equivalent • Proficiency with Git, GitHub, code review, and CI/CD • Experience with Docker and familiarity with cloud platforms; AWS preferred • Familiarity with STAC specifications and geospatial data catalog infrastructure • Strong written communication skills and ability to contribute to scientific manuscripts and technical documentation • Basic knowledge of forest ecology, remote sensing principles, or natural resource science • Ability to work collaboratively in interdisciplinary teams • Ability to self-motivate, manage time, and work independently in a remote-first environment • Ability to translate between scientific and engineering audiences • Must already be authorized to work in the U.S. without visa sponsorship • Preferred: Ph.D.; geospatial foundation models and self-supervised learning; Kubernetes and distributed computing; MLflow or W&B; PostgreSQL/PostGIS and message queues; relevant publications

🏖️ Benefits

• Health, dental, and vision insurance • 401(k) plan • Unlimited PTO policy • Company equity • Cell phone stipend (per pay period) • Home office setup allowance (one-time)