Staff Deep Learning Engineer, State Estimation

Accountabilities • Develop and evaluate deep learning models for feature detection and matching, visual correspondence, depth estimation, relative pose estimation, image-to-map localization, and related perception tasks.

• Combine learned visual representations with geometric estimation techniques to improve localization accuracy, robustness, and recovery in challenging conditions.

• Own data preparation and supervision strategies, including dataset curation, annotation requirements, labeling workflows, automated quality checks, and coverage analysis.

• Select and integrate appropriate deep learning tools while building reproducible training workflows with configuration management, experiment tracking, and dataset and model versioning.

• Design comprehensive evaluations covering both model-level performance and downstream localization outcomes across variations in lighting, viewpoint, altitude, terrain, weather, and sensor characteristics.

• Investigate model failures and use controlled experiments to identify and prioritize improvements to datasets, supervision strategies, architectures, and system integration.

• Partner with state estimation engineers to integrate learned measurements and confidence estimates into visual-inertial odometry (VIO) and terrain-relative navigation systems.

• Profile models against onboard compute, memory, and latency constraints and collaborate with deployment engineers on optimization and runtime validation.

• Deliver tested, documented software components and interfaces for an autonomy SDK while collaborating with software, systems, and flight-test teams.

• Communicate technical assumptions, experimental findings, limitations, and design tradeoffs clearly to research and engineering stakeholders.

• Help translate advanced computer vision and deep learning research into maintainable, tested, and deployment-ready capabilities.

Requirements

• 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.

Benefits

• Annual salary range of $200,000–$300,000 , with compensation influenced by experience, skills, certifications, and work location.

• Performance bonus opportunity.

• Equity package for eligible full-time employees.

• Comprehensive employee benefits package for eligible full-time employees.

• Remote work environment within the United States.

• Opportunity to work on advanced deep learning, computer vision, autonomy, and state-estimation challenges.

• Exposure to multidisciplinary collaboration across machine learning, robotics, software, systems, deployment, and flight-test engineering.

• Opportunity to contribute to production-grade autonomous systems and transform advanced research into deployed capabilities.

• Temporary employees may receive a temporary benefits package after 60 days, subject to applicable eligibility requirements.

• Offers are contingent upon a cleared background and, where applicable, reference checks.

• Equal employment opportunity and reasonable accommodation support.

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