Autonomous Navigation Engineer (Software & Machine Learning)

At Cyberwave, we're building the infrastructure layer for intelligent machines — making robotics as accessible, scalable, and programmable as cloud software.

Our mission is to make AI do dehumanizing work in factories and industrial environments around the world. To get there, robots must perceive, decide, and move autonomously — in complex, dynamic, GPS-denied environments.

We're looking for a Software & Machine Learning Engineer specialized in Autonomous Navigation to design and deploy real-world autonomy systems for flying and underwater robots.

This is not a research-only role. This is about building systems that fly, dive, localize, avoid obstacles, and complete missions reliably — outside the lab.

You will architect and implement autonomy stacks for flying drones (UAVs) in GPS-denied or partially observable environments, underwater drones (ROVs/AUVs) operating with limited sensing and noisy conditions, and edge-deployed autonomous navigation pipelines running directly on embedded systems.

You'll work across perception (visual-inertial odometry, SLAM, depth sensing, sonar integration), state estimation and sensor fusion, path planning and trajectory optimization, model-based and learning-based control, simulation-to-real validation, and real-time deployment on constrained hardware.

You'll collaborate closely with ML engineers, robotics engineers, and systems engineers to bridge simulation, digital twins, and physical robots.

What this role is not: not a purely academic research role, not a simulation-only position, not a "maintain legacy ROS nodes" job. You will build autonomy that runs on real flying and underwater machines.

If you've built drones that fly autonomously, deployed navigation stacks to edge hardware, or debugged SLAM at 2am because the robot drifted 3 meters — we want to talk. Send us your GitHub, your projects, and what you've actually built.