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.

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.