Staff Software Engineer - Computer Vision
Key Skills for This Role
Full Job Posting
Responsibilities
- Productionize and continuously improve Array's photogrammetry and radar image formation pipelines using industry best practices.
- Step into individual pipeline stages — bundle adjustment, dense matching, point cloud generation, geometric refinement, geo-referencing — to improve accuracy, robustness, and throughput.
- Develop and optimize database schemas and storage solutions for managing large-scale 3D geospatial data.
- Contribute to broader software engineering efforts across the company, including data infrastructure, analytics, and systems to deliver our satellite data to customers.
Basic Qualifications
5+ years of software engineering experience, with a substantial portion focused on computer vision.
Strong proficiency in Python and C++, experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (e.g. Docker, Kubernetes).
Strong foundation in geometric computer vision, spanning camera modeling, epipolar geometry, and multi-view reconstruction pipelines (stereo, SfM, MVS, bundle adjustment).
Excellent communicator, with the ability to make high-quality technical decisions in a startup environment.
Preferred Skills & Experience
Experience with geospatial concepts, remote sensing data, or robotics/autonomous vehicle sensor data
Familiarity with digital signal processing, radar algorithms, or SAR concepts
Familiarity with deep learning approaches to 3D vision (e.g. NeRF, 3D Gaussian Splatting, learned feature matching, or depth estimation)
Experience with C++ or CUDA for performance optimization
Ability to build visualization tools for geospatial or 3D data (Cesium, Three.js, or similar)
Additional information
Build What Matters: Create technology that serves customers and advances humanity
Innovate Together: Stay curious, collaborate openly, push boundaries as one team
Employee Centric: Put our people first with a culture of trust, respect, and opportunity
Join industry-leading experts to deliver unprecedented insights about our planet and build the future of Earth observation.
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