Responsibilities
- Apply foundational and advanced remote sensing techniques—spanning image analysis, geospatial analytics, machine learning, and deep learning—to extract insights from multispectral, hyperspectral, and SAR datasets.
- Build, validate, and operationalize predictive and prescriptive analytics models that improve efficiency, reliability, and outcomes within agricultural operations.
- Design and maintain scalable pipelines for imagery ingestion, preprocessing, feature engineering, modeling, and deployment.
- Produce clear, comprehensive documentation covering problem framing, data requirements, modeling methodology, validation, and operationalization.
- Adhere to data science best practices, including code reviews, reproducibility, testing, and version control.
Requirements
- Ph.D. or M.S. (with 4+ years of experience) in Remote Sensing, Geospatial Science, Data Science, Agronomy, Imagery & Robotics, or a related field.
- Hands-on expertise with remote sensing datasets and image processing techniques such as atmospheric correction, orthorectification, segmentation, and feature extraction.
- Proficiency with geospatial tools (e.g., GDAL, Rasterio, GeoPandas) and spatial data formats.
- Strong experience applying ML/DL approaches to imagery, including CNNs, semantic segmentation, object detection, time-series modeling, and emerging foundational models.
- Solid understanding of agricultural systems, crop development, environmental influences, and agronomic workflows, with the ability to contextualize imagery-derived insights.
- Experience using cloud environments (AWS, Azure, or GCP) for scalable data processing and deployment.
- Strong Python programming skills and familiarity with ML/DL frameworks (TensorFlow, PyTorch, OpenCV, scikit-learn). Proficiency in SQL and best practices in version control, testing, and reproducible workflows.
- Strong communication, collaboration, curiosity, and a high sense of ownership, particularly when translating complex findings for non-technical audiences.
Nice to Have
- Experience collaborating directly with agronomists, field teams, or scientists on agricultural analytics challenges.
- Familiarity with AI-assisted coding tools for rapid prototyping.
- Applied experience deploying production-grade geospatial ML/DL models in cloud environments.
- Background working with large-scale imagery workflows in operational settings.
- Demonstrated ability to drive innovation through new modeling, feature extraction, or remote sensing methodologies.
Compensation
The annual salary for this position is between $68,000 - $109,000 depending on experience and other qualifications of the successful candidate.
Work Arrangement
Remote (Country)
Additional Information
- Applications will be accepted until March 29, 2026.
- This role is not able to offer visa transfer or sponsorship now or in the future.
- We strive to provide flexibility wherever possible. This is a remote position open to qualified applicants in the United States.
- The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements.
