Image Processing Intern: AI-Assisted Tools for CalVal

Loft Orbital is looking for an intern to join the End-to-End Validation & Performance (EVP) team and contribute to image-processing and calibration/validation (CAL/VAL) activities for Earth-observation missions. In this internship, you’ll explore how AIT captures and routine onboard imagery can support optical performance testing and radiometric calibration, and turn that work into an evaluated engineering prototype.

• Review DSNU, radiometric calibration, and relevant EVP processing and CAL/VAL practices.
• Work with EVP engineers to identify suitable routine images, metadata, and reference calibration data; document data limitations and provenance.
• Prepare a reproducible dataset and establish reference baselines, including a holdout evaluation strategy that avoids tuning and testing on the same acquisitions.
• Implement a baseline estimator: a data-fit term combined with a regularizing prior.
• Investigate additional AI/ML methods only where suitable training or validation data are available; compare them against the baseline rather than assuming they are superior.
• Evaluate correction quality, residual fixed-pattern structure, stability across images, runtime, and failure cases.
• Package the workflow as a prototype script or notebook with plots, quality checks, and an auditable record of inputs, parameters, and outputs.
• Present findings and recommendations to EVP and CAL/VAL stakeholders; document what evidence would be needed before operational use.
The intern will gain experience in:
• Radiometric calibration and CAL/VAL for space-based optical imaging.
• Push-broom image characteristics and PRNU/DSNU correction.
• Applied optimization and, where appropriate, machine learning for image processing.
• Experimental design, reference-data limitations, and quantitative performance assessment.
• Building reproducible engineering tools and communicating technical evidence to mission teams.