The posting
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…
ML Engineer
POSITION DETAILS
Department: Engineering
Reports To: Engineering Manager, Infra Lead
Location: Remote, US | [Pacific / Mountain time zone preferred]
Employment: Full-time
ABOUT VIBRANT PLANET
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 vibrantplanet.net http://vibrantplanet.net and pyrologix.com http://pyrologix.com.
ABOUT THE ROLE
Vibrant Planet (https://www.vibrantplanet.net/) 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 SciDev and Data Engineering.
KEY RESPONSIBILITIES
ML Model Development & Adaptation
• 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.
Pipeline & Data Engineering
• Integrate trained ML models into Vibrant Planet’s automated geospatial data pipeline as containerized, orchestrated inference services.
• Build and maintain STAC (SpatioTemporal 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.
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.
• Participate in code reviews, architectural discussions, and sprint planning.
Team and Collaboration
• Demonstrated ability to work collaboratively in interdisciplinary teams spanning science, engineering, and product.
• Strong organizational skills to ensure high-quality data and clear documentation of workflows.
• Ability to self-motivate, manage time, and work independently in a remote-first environment.
• Excellent adaptive communication skills—ability to translate between scientific and engineering audiences.
• Commitment to an inclusive and equitable work environment where diverse views and backgrounds are valued.
• Comply with Vibrant Planet’s Information Security Policy and the full security responsibilities detailed in the Employee Handbook, including complete required security training, safeguard customer and company data, keep credentials secure, and report suspected security incidents or policy violations through established channels.
• Follow secure development practices, adhere to established change management processes for production systems, protect the confidentiality and integrity of customer data, and promptly address security vulnerabilities in your area of responsibility.
REQUIRED QUALIFICATIONS
• 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.
PREFERRED QUALIFICATIONS
• Ph.D. in a relevant field.
• Experience with geospatial foundation models and self-supervised learning.
• Experience with Kubernetes and distributed computing for large-scale inference.
• Familiarity with ML experiment tracking (MLflow, W&B) and model registry practices.
• Experience with database systems (PostgreSQL, PostGIS) and message queues.
• Publications in remote sensing, ML, or ecology journals.
COMPENSATION & BENEFITS
Salary Range: $100,000 – $200,000
• 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)
EQUAL OPPORTUNITY EMPLOYER
Vibrant Planet is committed to diversity. We encourage applicants from all cultures, races, colors, religions, sexes, national or regional origins, ages, disability status, sexual orientation, gender identity, military, or other status protected by law to apply.
We are most interested in finding the best candidate for the job, and that candidate may come from a less traditional background, but have capacity to grow into and thrive in the position after some mentoring. We do not require that you have experience with every job description task. We will consider any equivalent combination of knowledge, skills, education, and experience to meet minimum qualifications. We encourage each candidate to think broadly about their unique background and skill set and how it may relate to the role.
While we welcome applicants from all backgrounds, we regret that we are unable to provide visa sponsorship (including H-1B) at this time. Candidates must already be authorized to work in the U.S. without the need for sponsorship.
