Staff Deep Learning Engineer, State Estimation

This is a remote engineering opportunity focused on advancing autonomous systems that can understand their motion and localize themselves when GPS is unavailable or unreliable.
You will work at the intersection of deep learning, 3D computer vision, geometric estimation, and vision-based navigation.
The role gives you ownership of the machine learning lifecycle, from data strategy and annotation through model development, evaluation, integration, and deployment.
You will develop learned capabilities for feature matching, depth estimation, relative pose estimation, and image-to-map localization.
Your work will directly support robust localization across challenging environmental and sensor conditions.
You will collaborate closely with state estimation, software, systems, deployment, and flight-test engineers to turn research concepts into reliable production capabilities.
The position is well suited to an experienced deep learning engineer who enjoys solving complex perception and autonomy problems and translating research into deployable software.

• Master’s degree in Aerospace Engineering, Electrical Engineering, Robotics, Computer Science, or a related technical field, with at least 4 years of relevant professional experience, or a Ph.D. with at least 2 years of relevant experience.

• Hands-on experience designing, training, debugging, and evaluating deep learning models using PyTorch or an equivalent framework.

• Strong understanding of architecture selection, loss-function design, optimization, and data augmentation techniques that preserve geometric consistency.

• Solid foundations in camera models, coordinate transformations, projective geometry, and multi-view geometry.

• Practical experience in one or more relevant areas such as vision-based navigation, visual geolocation, Structure from Motion (SfM), SLAM, 3D reconstruction, depth estimation, or related computer vision fields.

• Strong Python programming skills and experience developing maintainable, reusable software.

• Demonstrated ability to take a computer vision capability from problem definition and raw sensor data through training, evaluation, and integration readiness.

• Experience building sensor-data pipelines covering ingestion, cleaning, filtering, deduplication, and dataset versioning.

• Experience creating reproducible machine learning workflows involving configuration management, experiment tracking, checkpointing, and GPU performance troubleshooting.

• Ability to design meaningful benchmarks, prevent data leakage across related sequences or locations, evaluate performance across operating conditions, and connect model metrics to downstream geometric or localization performance.

• Experience profiling inference latency and memory consumption and assessing accuracy-versus-compute tradeoffs.

• Ability to document model interfaces and preprocessing requirements and advise deployment teams on export, precision, and runtime optimization.

• Strong communication skills, with the ability to explain technical findings, assumptions, and tradeoffs clearly and translate research into practical engineering solutions.

• Preferred experience includes aerial imagery, geospatial data, elevation maps, or matching observations across different viewpoints, lighting conditions, seasons, or sensor modalities.

• Experience with model export, quantization, TensorRT, ONNX, or embedded compute platforms is advantageous.

• Experience validating perception or robotics systems on physical platforms is a plus.

• Relevant publications, open-source contributions, or demonstrated delivery of production computer vision systems are valued.

• Familiarity with feature correlation, cost volumes, matching techniques, stereo, optical flow, localization, or related correspondence methods is beneficial.

• Experience applying learned priors to scene geometry, depth, motion, or appearance is advantageous.

• Exposure to diffusion models or flow matching for computer vision, geometric inference, or conditional generation is a plus.

• Aerospace and/or defense industry experience is preferred.

• Strong deep learning and 3D vision foundations are the core requirement; candidates do not need to meet every preferred qualification.