Who We Are We're proud to be partnering with Neros , a defense technology company rebuilding America's drone industrial base. Neros designs and manufactures high-performance unmanned systems that are combat-tested, rapidly iterated, and built for scale. Their mission is urgent and clear: close the gap between design and deployment to equip warfighters with the tools they need in months-not years. As drones reshape the nature of warfare, Neros is delivering the systems the West needs to maintain a strategic edge and deter adversaries.

What You Will Be Doing As an Autonomy Computer Vision Engineer, you'll be responsible for developing and implementing advanced computer vision algorithms for onboard use in autonomous drones. Your work will involve designing, testing, and optimizing vision systems that perform in real time and under demanding environmental conditions. You'll work closely with teams across robotics, software, and hardware to ensure seamless integration into autonomous platforms.

Key responsibilities include: Developing real-time computer vision algorithms for deployment on autonomous systems Implementing solutions using libraries and tools like OpenCV, TensorFlow, and PyTorch Working with techniques such as SLAM, object detection, and image segmentation Testing and debugging algorithms in both simulation and real-world environments Collaborating with cross-functional teams to align vision systems with navigation and autonomy requirements You Should Have the Following 3+ years of experience in computer vision, robotics, or a related field Proven track record building algorithms for real-time or embedded systems Proficiency in Python and C++ Expertise with computer vision frameworks such as OpenCV, TensorFlow, PyTorch, or similar Experience with SLAM, object detection, and image segmentation Familiarity with UAVs and autonomous drone systems Strong understanding of real-time system constraints and performance requirements Skilled in debugging, validating, and optimizing algorithms in both simulation and real-world settings