Waymo is looking to improve the accuracy and robustness of its core state estimation algorithm for its autonomous driving technology, aiming to enhance the safety and efficiency of its Waymo One ride-hailing service.
Requirements
- Expertise in state estimation theory and application, including Kalman Filters, particle filters, factor graphs, and nonlinear optimization techniques.
- Deep understanding of inertial navigation principles, including sensor modeling, IMU error characterization, calibration, and multi-sensor fusion with GNSS and other aiding sensors.
- Strong C++ skills and ability to write high quality production level code.
- Experience with computer vision, visual inertial odometry, SLAM, ICP or other sensor modalities.
- Experience developing and using tools for large-scale data analysis and visualization (e.g., SQL, Python with libraries like NumPy, Pandas, Matplotlib).
- Familiarity with machine learning techniques applied to sensor fusion, sensor calibration, or positioning problems.
Responsibilities
- Develop algorithms to improve the accuracy and robustness of our core state estimation algorithm.
- Engineer and deploy robust, production-quality C++ software to fuse data from inertial, GNSS, and other advanced sensors into our algorithm for both new and existing vehicle platforms.
- Build and maintain tools, pipelines, and evaluation frameworks to rigorously assess positioning system performance across millions of miles of real-world driving data.
- Leverage Waymo's massive datasets to conduct large-scale analyses and optimizations, fine-tune sensor models, characterize system performance, identify root causes of failures, and establish performance guarantees.
- Collaborate closely within a multi-disciplinary team of engineers to solve complex technical challenges, contribute to a culture of high standards, and share your expertise with fellow team members.
Other
- MS, PhD or equivalent industry experience.
- Previous experience in the autonomous vehicle industry, particularly in inertial navigation, localization, perception, or sensor fusion.
