
Computer Vision Engineer
2 weeks ago
This role is for a
Computer Vision Engineer
with a specialization in
Remote Sensing
and
Geospatial Analysis
. The primary responsibility is to develop and implement machine learning models to detect and identify objects in satellite imagery. This involves leveraging a strong foundation in computer vision, deep learning, and geospatial data processing to create solutions that can learn from minimal data.
Responsibilities
- Design and implement machine learning models
for object detection and identification using satellite imagery, focusing on architectures like YOLO, R-CNN, and SSD. - Develop and apply advanced techniques
such as few-shot learning and self-supervised methods to enable model adaptation with limited labeled data. - Process and manage large-scale geospatial datasets
, including cleaning, manipulating, and annotating data from various satellite sensors. - Collaborate with team members
using version control (Git) to maintain a robust and well-documented codebase. - Research and evaluate new algorithms
and deep learning techniques to improve model performance and solve complex problems. - Utilize cloud platforms
(e.g., AWS, GCP) for data storage and computational tasks to efficiently process large image datasets.
Qualifications
- Strong proficiency in Python
and its core libraries for data science and machine learning, including NumPy, Pandas, Scikit-learn, and OpenCV. - Hands-on experience
with at least one major deep learning framework, such as PyTorch or TensorFlow. - Solid understanding of computer vision fundamentals
, including image processing, feature extraction, and segmentation. - Knowledge of geospatial concepts
and tools, including GIS, coordinate systems, and libraries like GDAL, Rasterio, or Shapely. - Familiarity with data annotation tools
(e.g., CVAT, LabelImg) and version control systems (Git). - Ability to conduct independent research
and solve complex technical challenges with minimal supervision.
Definition of Success
Success in this position is defined by the ability to
develop and deploy accurate, high-performing models
that can identify objects in satellite imagery, even when provided with only a few examples. A successful engineer will not just train a model, but will
creatively leverage advanced techniques
like few-shot learning and self-supervised methods to overcome data scarcity. They will be adept at handling the unique challenges of satellite data, such as large file sizes and complex projections, using cloud-based tools efficiently. Ultimately, success means delivering a
scalable and well-documented solution
that provides valuable insights from a variety of remote sensing sources.
Impact of the Role
This role is a critical component of the organization's core mission to extract actionable intelligence from geospatial data. This engineer will be at the forefront of innovation,
bridging the gap between raw satellite data and meaningful insights
.
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