Are you passionate about developing cutting-edge SLAM algorithms for autonomous robots? Do you thrive on iterative research and development of localization and mapping algorithms for autonomous motion? Is your expertise centered around crafting robust on-robot software that drives autonomy and reliability?

At Collaborative Robotics, the development of on-robot software is at the core of our mission to create highly reliable, fully autonomous, and collaborative robots. As a Sr. Software Engineer in Localization and Mapping, you will play a crucial role in enhancing our current software stack and driving the design and implementation of next-generation robotic capabilities.

Collaborative Robotics is a team of innovators and builders redefining the future of human-robot interaction. We are working to realize a world where robots are a trusted extension of your surroundings. They work, adapt, and react around you. Not the other way around.

This role is located at our Santa Clara, CA headquarters.

Key Responsibilities:

  • Develop SLAM algorithms for an indoor mobile robot.
  • Enhance mapping capability for long-term autonomous operation.
  • Expand mapping capability to support large floor plans including multi-floor buildings.
  • Implement, debug, test, and maintain SLAM software.
  • Develop sensor calibration techniques for both end-of-line and live updates.
  • Root cause localizations failures and bugs and use learnings to improve software and SLAM validation.
  • Create reliable robotic system performance through robust algorithm development, success measurement, and recovery actions.

Minimum Qualifications: 

  • M.S. in computer science, robotics, electrical or other engineering disciplines.
  • 6+ years of full-time work experience, or 4+ years with PhD, on industry engineering teams.
  • Proficient in state estimation, visual-inertial odometry, visual SLAM, and multiple view geometry.
  • Experience with EKF, pose graph optimization and bundle adjustment.
  • Experience writing production level code in C++.
  • Strong 3D geometry fundamentals.
  • Knowledge of Lidar-based SLAM and point-clouds.
  • Highly motivated teammate with excellent oral and written communication skills.
  • Enjoy working in a fast paced, collaborative and dynamic start-up environment as part of a small team.
  • Willing to occasionally travel.
  • Must have and maintain US work authorization.

Preferred Qualifications:

  • Ph.D. in computer science, robotics, electrical or other engineering disciplines.
  • Experience developing SLAM on an indoor mobile robot.
  • Experience with ROS2.
  • Knowledge of python.
  • Experience with sensor calibration.
  • Experience with Nvidia Jetson platforms including CPU programming.

The base salary range for this position is $205,000 - $240,000 plus equity and comprehensive benefits. Our salary ranges are determined by role and experience level. The range reflects the minimum and maximum target for new hire salaries for the position in Santa Clara, CA. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training.

Collaborative Robotics is committed to a diverse and inclusive workplace. Collaborative Robotics is an equal opportunity employer and does not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We are committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let your recruiter know.

To all recruitment agencies: Collaborative Robotics does not accept agency resumes. Please do not forward resumes to our employees. Collaborative Robotics is not responsible for any fees related to unsolicited resumes.

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