BRINC
Autonomy Engineer, Navigation
About this role
BRINC is seeking a Computer Vision Engineer to advance visual perception and autonomy for UAVs deployed in public safety operations. You'll design and optimize real-time vision algorithms for localization, mapping, and navigation in challenging environments, working across VIO, VSLAM, and scene understanding while collaborating with autonomy and hardware teams.
What you'll do
- Design and implement vision-based localization and mapping algorithms including VIO and VSLAM
- Develop real-time computer vision pipelines for tracking, depth estimation, reconstruction, and obstacle detection
- Architect sensor fusion systems combining cameras, IMUs, LiDAR, and radar for robust perception
- Build perception models for scene understanding, object detection, and dynamic obstacle identification
- Optimize CV pipelines for embedded GPU and accelerator platforms with focus on latency and performance
- Validate perception systems through simulation, hardware-in-the-loop, and real-world flight testing
What they're looking for
- C++ and Python programming for real-time systems
- VSLAM, VIO, and visual localization techniques
- Computer vision frameworks and modern perception methods
- Embedded systems and GPU optimization
- Sensor fusion and vision-IMU systems
- Machine learning model optimization for edge hardware
- ROS, PX4, or similar robotics middleware
- UAV testing, debugging, and field validation
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
BRINC
BRINC builds drone systems and cloud platforms that enable public safety agencies to deploy drones to emergency response situations like 911 calls. The company is hiring for engineering roles across navigation and sensing, electrical systems, RF communications, embedded software, and fullstack web development to support their drone hardware and LiveOps platform.
- Website
- brinc.io
Likely interview questions
- Walk us through your experience developing planning or perception algorithms for unmanned systems. What were the key challenges and how did you address computational constraints?
- Describe your approach to obstacle detection and avoidance. How have you integrated sensor data (radar, camera, lidar) into a real-time navigation pipeline?