Deep Learning Engineer
US$200,000 – US$300,000 / yr base salary
Posted 26 Sep 2026
Advertised as “Staff Deep Learning Engineer, State Estimation (R5785)”
BranchFactor Summary
The original posting with the fluff stripped out
- Level
- R5785
- Expected experience
- 4+ yrs
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
| Base salary | US$200,000 – US$300,000 / yr |
|---|
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
Job Description:
Join Shield AI’s Hivemind SDK State Estimation & Vision team to build deep learning capabilities that help autonomous systems understand their motion and localize in the world when GPS is unavailable or unreliable.
You will work at the intersection of deep learning, 3D computer vision, and geometric estimation, developing learned components for vision-based navigation. You will own the model development pipeline—from selecting tools, defining annotation needs, and cleaning data through training, evaluation, integration support, and deployment recommendations.
Hands-on experience with Deep Learning and 3D computer vision and geometry is required; prior experience specifically in vision-based navigation is optional.
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.