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Glydways is building a revolutionary autonomous transit system—carbon-neutral, interconnected pathways powered by standardized autonomous vehicles (Glydcars) on dedicated roadways. The company is reimagining public transportation to be more accessible, affordable, and sustainable.
You will join the Localization Systems team, responsible for developing software for localization, calibration, and mapping of robotic ground vehicles. The team specializes in sensor fusion (LIDAR, RADAR, vision processing, UWB radios, inertial sensors), SLAM (Simultaneous Localization and Mapping), optimization, and filtering techniques. Team members develop C++ onboard software, conduct peer reviews, write unit/subsystem/end-to-end tests, and perform data analysis using Python. The autonomy stack must operate reliably in GPS-denied environments.
Key responsibilities include:
- Develop deep fluency with the localization team's C++ codebase and designs at the source-code level.
- Own the algorithm and test requirements capture process, conducting requirements reviews with autonomy software, systems, and safety teams.
- Write and own simulation and track test scenarios that validate localization systems, including critical safety functions.
- Write and own Hardware-in-the-Loop (HIL) testing for the localization team.
- Champion strong unit test coverage across the codebase.
- Pursue systems-level solutions to unblock algorithm and hardware work for other developers.
- Collaborate with safety and neighbor teams to fine-tune localization and safety algorithm configuration settings.
- Conduct and receive code reviews and requirements reviews, demonstrating attention to detail and comprehension of complex systems.
REQUIREMENTS:
Required:
- Experience with requirements management tools (e.g., Jama) and structuring test coverage across unit, simulation, track, and HIL levels.
- Familiarity with safety qualification standards related to localization accuracy.
- MSc in Mechanical Engineering, Electrical Engineering, Computer Science, or a related systems engineering field. Does not need to be highly senior, but relevant, demonstrable experience is required.
- Hands-on and flexible/adaptable—willing to get into the details and open to changing approach based on feedback.
Desired:
- Strong background in localization algorithms/estimation theory including Kalman filtering and/or pose graph optimization.
- Familiarity with LIDAR- and computer-vision-based localization, mapping, and calibration; radar experience a plus.
- Proficiency in C++ sufficient to understand and navigate the team's source code.
