About the job
About BRINC:
At BRINC, we are on a mission to revolutionize public safety through an innovative suite of life-saving technologies. Our journey began with the creation of drones and robust throw phones designed for safe communication in hazardous environments. Today, we have expanded our capabilities to develop and deploy 911 response networks, where drones provide real-time visual data to enhance safety and facilitate effective de-escalation of critical situations. Our pioneering solutions are currently in use by over 600 public safety agencies across the country, and we have successfully raised over $150 million from prestigious investors, including Index Ventures, Motorola Solutions, Sam Altman, Dylan Field, Mike Volpe, and Alexandr Wang. At BRINC, we are dedicated to attracting exceptional talent to join us in our mission to support first responders in their life-saving efforts.
About this Role:
We are looking for a talented Computer Vision Engineer to join our Autonomy engineering team. In this role, you will play a pivotal part in enhancing the visual perception and vision-based autonomy capabilities of our UAVs and public safety technologies. You will be responsible for designing, implementing, and optimizing cutting-edge computer vision algorithms that enable precise localization, mapping, and visual navigation in challenging environments. Collaborating closely with software, autonomy, controls, and hardware teams, you will bring vision algorithms into production UAV systems, working on VIO, VSLAM, depth and reconstruction pipelines, and visual scene understanding.
Key Responsibilities:
Conduct research and design vision-based localization and mapping algorithms, including VIO and VSLAM techniques.
Develop real-time computer vision pipelines for tracking, depth estimation, stereo/mono reconstruction, and dense/semi-dense mapping.
Architect and optimize vision-centric sensor fusion systems that integrate cameras, IMUs, LiDAR, radar, and other sensors to ensure robustness in diverse environments.
Create perception algorithms that facilitate vision-based navigation, including feature tracking, obstacle detection, and perception-driven flight behaviors.
Develop computer vision and machine learning models for scene understanding, object detection, and dynamic obstacle recognition.
Implement and optimize CV pipelines on embedded GPU or accelerator platforms with a focus on high performance and low latency.
Validate perception and autonomy performance through simulation, hardware-in-the-loop, and real-world flight testing.
Collaborate with cross-functional teams to integrate vision capabilities into existing and future UAV systems.

