About the role

We're hiring a Senior Autonomy Engineer specializing in VIO/VI-SLAM to work directly with our CTO improving our vision-based indoor autonomy stack. Our drones and robots help warehouses manage inventory, powered by our state-of-the-art autonomy engine. They operate 24x7 without any human intervention, running every day in customer warehouses and factories around the country.

We're looking for someone who loves early-stage startups, working on a small and close-knit engineering team, and can help lead complex projects from start to finish. If you are interested in working on challenging visual-inertial localization problems which form the basis for safety-critical autonomy -- with rapid and continuous feedback from a fleet of field-deployed robots -- this role is for you!

This is an opportunity to join a core team building for one of the world's largest markets for indoor robotics: inventory management. Warehouses touch every physical object around you, from the personal items you own to the building itself, and inventory management is a core process every warehouse and factory does globally. You should have at least several years of experience putting visual-inertial localization (VIO/VISLAM) systems onto production robots or resource-constrained edge devices (e.g AR/VR headsets, mobile phones), architecting and implementing ideas which go beyond academic state-of-the-art, and mentoring and helping lead a small team.

Must have

• Fully remote ok -- our office is in Mountain View, CA
• 4+ years in visual-inertial odometry/SLAM, having put it into production
• Experience with camera/IMU models and calibration
• Expertise in
• C++
• Git
• Linux
• OpenCV
• Nonlinear optimization with GTSAM, Ceres Solver, g2o or similar
• Has led a small team with check-ins, communicating status with rest of leadership
• Has led/architected a SLAM system from initial proof of concept to production shipping
• Has worked at an early stage startup before (

Nice to have

• Performance engineering, SIMD-oriented programming (SSE, AVX)
• PyTorch or other deep-learning training frameworks
• Intel OpenVINO or other deep-learning inference acceleration frameworks
• Experience with PX4 or similar deeply-embedded middleware, or bare-metal embedded systems
• BS in Computer Science, Aerospace/Aeronautics, Robotics or similar (NOT a requirement)