Research Engineer – Computer Vision

Overview

• Lead advancements in geospatial computer vision research, driving innovation in satellite imagery analysis and contributing to impactful projects in a dynamic, remote environment.
• Design and implement robust evaluation frameworks to ensure accuracy and reliability in geospatial computer vision systems.
• Develop and maintain benchmark datasets for tasks such as object detection, segmentation, and geospatial feature extraction.
• Create automated evaluation pipelines and tools for model performance analysis and comparison across datasets.
• Collaborate with multidisciplinary teams to analyze high-resolution satellite imagery and enhance model capabilities.
• Mentor engineers and researchers, fostering best practices in evaluation systems and experimentation.
• Support experimentation with cutting-edge computer vision models and geospatial machine learning architectures.
• Contribute to the development of internal tools for model output inspection, debugging, and dataset exploration.

Key Responsibilities & Duties

• Define and implement evaluation metrics and ground truth standards for geospatial computer vision systems.
• Design and curate benchmark datasets and protocols for geospatial computer vision tasks.
• Develop automated pipelines for model evaluation and publish experiment results to centralized systems.
• Create tools and dashboards for model analysis, debugging, and dataset exploration.
• Collaborate with research teams to identify failure modes and propose solutions for model improvement.
• Support experimentation with state-of-the-art computer vision models and architectures.
• Mentor team members on evaluation system design and machine learning experimentation best practices.
• Establish organizational standards for reproducibility, benchmarking, and validation of machine learning models.

Job Requirements

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