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Staff Deep Learning Engineer, State Estimation

🕒 September 26

🇺🇸 United States – Remote

💵 $200k - $300k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

Where this job comes from

Posted on Shield AI's careers page, not through a job board

First seen by Remote Rocketship September 26

Still open on Shield AI's site. Checked 3 hours ago.

Logo of Shield AI

Shield AI

501 - 1000 employees

Founded 2015

🤖 Artificial Intelligence

🚀 Aerospace

🎖️ Defense

Artificial Intelligence • Aerospace • Defense

Shield AI is a leading developer of AI-driven military solutions, focusing on enhancing mission autonomy and battlefield awareness. Their platform, Hivemind, enables rapid deployment of intelligent systems for various defense applications, including drone operation and surveillance. With a commitment to utilizing advanced technology, Shield AI aims to protect service members and civilians by revolutionizing defense technologies through autonomous systems.

📋 Description

• Develop and evaluate models for 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 • 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 with experiment tracking, configuration management, and dataset/model versioning • Design evaluations across changes in lighting, viewpoint, altitude, terrain, weather, and sensor characteristics • Analyze failures and 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 support optimization and runtime validation • Deliver tested, documented components and interfaces for Hivemind SDK • Collaborate with software, systems, and flight test teams

🎯 Requirements

• 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 • Strong foundations in camera models, coordinate transformations, projective geometry, and multi-view geometry • Practical experience in one or more of: vision-based navigation, visual geolocation, Structure from Motion (SfM), SLAM, 3D reconstruction, depth estimation or similar fields • Strong Python skills and experience writing maintainable, reusable software • Experience taking computer vision capabilities 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, 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 on export, precision, and runtime optimization • Ability to communicate assumptions, experimental findings, and design tradeoffs clearly and translate research into working software • All offers contingent on a cleared background and possible reference check

🏖️ Benefits

• Bonus • Benefits • Equity • Temporary benefits package applicable after 60 days of employment for temporary employees

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