Research Engineer - Computer Vision

Key Responsibilities

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Model Evaluation & Ground Truth SystemsOwn the evaluation definitions, metrics, and ground truth standards for computer vision systems operating on satellite imagery.Design and maintain benchmark datasets and evaluation protocols for tasks such as:Object detectionChange detectionSegmentationGeospatial feature extractionEnsure evaluation frameworks reflect real-world geospatial use cases and edge cases.
Evaluation InfrastructureBuild and maintain evaluation harnesses that automatically run experiments across models and datasets.Publish experiment results to a centralized system (e.g., Weights & Biases or similar experiment tracking tools).Develop reproducible pipelines that enable researchers to compare model performance across versions and datasets.
Internal Tooling & DashboardsDevelop lightweight internal tools and dashboards that allow engineers and researchers to:Inspect model outputsCompare predictions vs. ground truthExplore dataset slicesDebug failure modesBuild review and triage systems that streamline model analysis workflows for CV researchers.
Applied Computer Vision Research SupportCollaborate with research teams to analyze model performance on high-resolution satellite and aerial imagery.Help identify systematic failure modes and propose evaluation-driven improvements.Support experimentation with state-of-the-art CV models including foundation models, segmentation networks, and geospatial ML architectures.
Cross-Team ImpactEstablish best practices for evaluation and ML experimentation across the organization.Provide technical leadership in reproducibility, benchmarking, and model validation.Mentor engineers and researchers in designing reliable evaluation systems.
Required Qualifications7+ years of experience in machine learning, computer vision, or applied AI engineering.Strong experience in computer vision model development and evaluation.Experience working with satellite, aerial, or geospatial imagery (or similarly large-scale visual datasets).Proven experience building ML evaluation harnesses or benchmarking systems.Experience publishing experiment results to a centralized tracking system (e.g., Weights & Biases, MLflow, or similar).Experience building internal developer tools, dashboards, or analysis systems.Strong programming skills in Python and familiarity with common CV/ML frameworks (PyTorch, TensorFlow, etc.).Experience designing metrics, ground truth schemas, and evaluation protocols for ML systems.
Preferred QualificationsExperience in the geospatial or Earth observation industry.Familiarity with remote sensing data formats and geospatial libraries (e.g., raster data pipelines, GIS tools).Experience working with large-scale imagery datasets (terabytes to petabytes).Experience evaluating foundation models or multimodal vision models.Familiarity with Weights & Biases (W&B) or similar experiment management platforms.Experience designing model debugging or annotation review interfaces. xzbcwsv Experience working in research-oriented ML teams.