Autonomy Engineer, Computer Vision

Key Responsibilities:

• Research, design, and implement vision-based localization and mapping algorithms, including VIO and VSLAM.
• 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 combining cameras, IMUs, LiDAR, radar, and other sensors for robustness in diverse environments.
• Build perception algorithms enabling 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 identification.
• Implement and optimize CV pipelines on embedded GPU or accelerator platforms, focusing 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 ensure seamless integration with autonomy, controls, mechanical, and firmware systems.
• Contribute to technical strategy, establish best practices, and help guide the evolution of the perception stack as the system scales and matures.

Qualifications:

• Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Electrical Engineering, or a related field with a minimum of 3+ years of industry experience. Note - we are considering candidates for mid-career, senior, and principal positions.
• Strong programming skills in C++ and Python, with experience building real-time systems.
• Experience developing computer vision or perception systems for robotics or UAVs, with a foundation in VSLAM, VIO, and/or related topics. Proficiency with standard frameworks and modern computer vision techniques.
• Familiarity with implementing CV models or pipelines on embedded systems, GPUs, or hardware accelerators.
• Hands-on experience with robotics or UAV testing, including data collection, system debugging, and field validation.

Preferred Skills:

• Deep knowledge of sensor fusion and tightly coupled vision–IMU systems.
• Experience with machine learning–based perception, including training and optimizing deep models for edge hardware.
• Background in vision-based navigation, visual servoing, or perception-driven autonomy.
• Strong understanding of real-time systems, GPU optimization, or high-performance computer vision.
• Familiarity with UAV safety, reliability, and regulatory considerations for autonomous systems.
• Ability to help shape architecture, mentor engineers, and drive cross-functional technical decisions as needed.
• Experience with ROS, PX4, MAVSDK, and/or similar robotics middleware.

BRINC Culture Values: