Staff Deep Learning Engineer, State Estimation

Location: Remote

Company

hirify.global develops autonomy software and systems that help aircraft and other autonomous platforms operate in complex environments, including when GPS is unavailable or unreliable.

What you will do

• Develop and evaluate deep learning models for feature detection and matching, depth estimation, relative pose estimation, and image-to-map localization.
• Combine learned visual representations with geometric methods to improve localization accuracy, robustness, and recovery.
• Own data preparation and supervision, including dataset curation, annotation requirements, labeling tools, quality checks, and coverage analysis.
• Build reproducible training workflows with tool integration, experiment tracking, configuration management, and dataset and model versioning.
• Design evaluations, analyze failures, and connect model metrics with downstream geometric and localization outcomes across varied operating conditions.
• Integrate learned measurements into VIO and terrain-relative navigation systems, profile models against onboard constraints, and deliver tested components for the Hivemind SDK.

Requirements

• M.S. in aerospace engineering, electrical engineering, robotics, computer science, or a related field, with 4+ years of related experience; or a Ph.D. with 2+ years of experience.
• Hands-on experience designing, training, debugging, and evaluating models with PyTorch or an equivalent framework.
• Strong foundations in camera models, coordinate transformations, projective geometry, and multi-view geometry, with practical experience in areas such as vision-based navigation, visual geolocation, SfM, SLAM, 3D reconstruction, or depth estimation.
• Strong Python skills and experience taking computer vision capabilities from problem definition and raw data through training, evaluation, and integration readiness.
• Experience with sensor-data pipelines, dataset versioning, reproducible workflows, benchmarking, data-leakage prevention, and GPU performance troubleshooting.
• Ability to profile inference latency and memory use, assess accuracy–compute tradeoffs, document interfaces, and communicate experimental findings and design tradeoffs clearly.

Nice to have

• Experience with aerial imagery, geospatial data, elevation maps, or matching observations across different viewpoints, lighting, seasons, or sensor modalities.
• Experience with model export, quantization, TensorRT, ONNX, or embedded compute platforms.
• Experience validating perception or robotics systems on physical platforms.
• Relevant publications, open-source contributions, or production computer vision systems, including feature correlation, cost volumes, depth or motion priors, diffusion models, or flow matching.
• Experience in the aerospace or defense industry.

Culture & Benefits

• Remote work arrangement.
• Collaboration with state estimation, software, systems, deployment, and flight test engineers.
• Work on autonomy capabilities for the Hivemind SDK and physical autonomous platforms.