Machine Learning Engineer

Job not on LinkedIn

🕒 August 14

🇺🇸 United States – Remote

💵 $100k - $200k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 34%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Vibrant Planet

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11 - 50 employees

🌾 Agriculture

💰 $17M Seed Round on 2022-06

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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

• Adapt and fine-tune custom or publicly available geospatial foundation models as backbone architectures for domain-specific deep neural network heads estimating forest structure metrics • Prepare, curate, and manage training datasets from Sentinel-2, Sentinel-1, Landsat, lidar, NAIP, and field plot inventories • Evaluate model performance using remote sensing accuracy metrics and field-based validation data • Contribute to experiment design, hyperparameter optimization, and ablation studies • Integrate trained ML models into the 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 smaller Airflow DAGs, 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 • Develop model cards summarizing modeling methods and performance • 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 • Document pipelines, model architectures, and operational procedures • Participate in code reviews, architectural discussions, and sprint planning • Comply with information security policies, complete required security training, safeguard data and credentials, report suspected incidents, and follow secure development and change management practices

🎯 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 data science stack (NumPy, pandas, xarray, scikit-learn) • 3+ years of experience with geospatial data processing (rasterio, GDAL, geopandas, shapely) • Experience building and maintaining data pipelines with workflow orchestration tools (Airflow, Prefect, Dagster, or equivalent) • Proficiency with Git, GitHub, and collaborative software development practices (code review, CI/CD) • Experience with containerization (Docker) and familiarity with cloud platforms (AWS preferred) • Familiarity with STAC specifications and geospatial data catalog infrastructure • Strong written communication skills; ability to contribute to scientific manuscripts and technical documentation • Basic knowledge of forest ecology, remote sensing principles, or natural resource science • Ph.D. in a relevant field (preferred) • Experience with geospatial foundation models and self-supervised learning (preferred) • Experience with Kubernetes and distributed computing for large-scale inference (preferred) • Familiarity with ML experiment tracking (MLflow, W&B) and model registry practices (preferred) • Experience with database systems (PostgreSQL, PostGIS) and message queues (preferred) • Publications in remote sensing, ML, or ecology journals (preferred) • Must already be authorized to work in the U.S. without visa sponsorship

🏖️ 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)

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