Research Engineer – Computer Vision

Overview

• Contribute to cutting-edge computer vision research and development for satellite imagery applications in a permanent, remote role.
• Design and implement evaluation frameworks, metrics, and ground truth standards for geospatial computer vision systems.
• Develop and maintain benchmark datasets for object detection, change detection, segmentation, and geospatial feature extraction tasks.
• Build automated evaluation harnesses and pipelines for model performance comparison across versions and datasets.
• Create internal tools and dashboards for model output inspection, debugging, and dataset exploration.
• Collaborate with research teams to analyze high-resolution satellite imagery and improve model performance.
• Mentor engineers and researchers in designing reliable evaluation systems and establishing best practices.
• Support experimentation with state-of-the-art computer vision models and geospatial ML architectures.

Key Responsibilities & Duties

• Define evaluation metrics and ground truth standards for computer vision systems on satellite imagery.
• Design benchmark datasets and protocols for geospatial computer vision tasks.
• Develop automated evaluation pipelines and publish experiment results to centralized tracking systems.
• Create tools and dashboards for model analysis, debugging, and dataset exploration.
• Collaborate with research teams to identify failure modes and propose improvements.
• Support experimentation with advanced computer vision models and architectures.
• Mentor team members in evaluation system design and ML experimentation best practices.
• Establish organizational standards for reproducibility, benchmarking, and model validation.

Job Requirements

• Master of Science (MS) degree in a relevant field.
• 7+ years of experience in machine learning, computer vision, or applied AI engineering.
• Proven expertise in computer vision model development and evaluation.
• Experience with satellite, aerial, or geospatial imagery and large-scale visual datasets.
• Proficiency in Python and familiarity with CV/ML frameworks like PyTorch and TensorFlow.
• Experience with experiment tracking systems such as Weights & Biases or MLflow.
• Knowledge of geospatial libraries and remote sensing data formats.
• Ability to design metrics, ground truth schemas, and evaluation protocols for ML systems.