Staff Deep Learning Engineer, State Estimation (R5785)

What you'll do:

• Develop and evaluate models for tasks such as feature detection and matching, visual correspondence, depth estimation, relative pose estimation, and image-to-map localization.
• Combine learned visual representations with geometric methods to improve localization accuracy, robustness, and recovery under challenging conditions.
• Own data preparation and supervision strategies, including dataset curation, annotation requirements, labeling tools, automated quality checks, and coverage analysis.
• Select and integrate deep learning tools and build reproducible training workflows, including experiment tracking, configuration management, and dataset and model versioning.
• Design evaluations that measure both model performance and downstream localization outcomes across changes in lighting, viewpoint, altitude, terrain, weather, and sensor characteristics.
• Analyze failures and use controlled experiments to prioritize improvements to data, supervision, models, and integration.
• Partner with state estimation engineers to integrate learned measurements and confidence estimates into VIO and terrain-relative navigation systems.
• Profile models against onboard compute, memory, and latency constraints, and work with deployment engineers on optimization and runtime validation.
• Deliver tested, documented components and interfaces for Hivemind SDK, collaborating with software, systems, and flight test teams.