Staff Deep Learning Engineer, State Estimation (R5785)

Job Description:

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.

Required qualifications:

• M.S. in Aerospace Engineering, Electrical Engineering, Robotics, Computer Science, or a related field; minimum 4+ years of related professional work experience with an M.S. degree, or 2+ years with a Ph.D.
• Hands-on experience designing, training, debugging, and evaluating models using PyTorch or an equivalent framework, including architecture selection, loss design, optimization, and augmentation that preserves geometric consistency.
• Strong foundations in camera models, coordinate transformations, projective geometry, and multi-view geometry, with practical experience in one or more fields: vision-based navigation, visual geolocation, Structure from Motion (SfM), SLAM, 3D reconstruction, depth estimation or similar fields. Expertise in every area is not required.
• Strong Python skills and experience writing maintainable, reusable software. Demonstrated ability to take a computer vision capability from problem definition and raw data through training, evaluation, and integration readiness.
• Experience building pipelines for sensor data ingestion, cleaning, filtering, deduplication, and dataset versioning.
• Ability to select and integrate development tools and build reproducible training workflows, including configuration management, experiment tracking, checkpointing, and GPU performance troubleshooting.
• Experience designing benchmarks, preventing data leakage across related sequences or locations, analyzing performance across operating conditions, and connecting model metrics to downstream geometric or localization accuracy.
• Ability to profile inference latency and memory use, document model interfaces and preprocessing, assess accuracy–compute tradeoffs, and advise deployment engineers on export, precision, and runtime optimization.
• Ability to communicate assumptions, experimental findings, and design tradeoffs clearly and translate research into working software.

Preferred qualifications:

• Experience with aerial imagery, geospatial data, elevation maps, or matching observations across viewpoint, lighting, season, or sensor modality
• Experience with model export, quantization, TensorRT, ONNX, or deployment on embedded compute platforms.
• Experience validating perception or robotics systems on physical platforms.
• Relevant publications, open-source contributions, or demonstrated delivery of production computer vision systems, familiar with methods including:
• feature correlation, correlation or cost volumes, and matching methods for correspondence, stereo, optical flow, or localization.
• learning priors over scene geometry, depth, motion, or appearance to improve estimation under sparse, ambiguous, or degraded observations.
• applying diffusion models or flow matching to computer vision, geometric inference, or conditional generation.
• Experience in aerospace and / or defense industry.
You do not need every nice-to-have to succeed in this role. We welcome engineers with strong Deep Learning and 3D vision foundations who are excited to develop expertise in state estimation and navigation to apply.