Senior Software Engineer, Calibration
- Lead the design, implementation, and delivery of next-generation online (dynamic) sensor calibration algorithms, proactively resolving deficiencies and ensuring that solutions meet objectives for reliability, quality, and business impact.
- Establish validation pipelines to guarantee that the developed online sensor calibration algorithm adheres to the required calibration accuracy specified by Toyota products.
- Optimize sensor calibration algorithms with respect to runtime, accuracy, and robustness, specifically considering constraints imposed by limited computational resources.
- Collaborate with vehicle engineers and operators to ensure exceptional sensor calibration quality, which is critical for optimal vehicle deployment and the enhancement of autonomous driving performance.
- Operate within a high-velocity environment and employ rigorous agile development practices.
- Work within a hybrid workspace model, necessitating physical presence in our Ann Arbor (Michigan, US) office three days per week.
- A minimum of three years of industrial experience in computer vision or a related field, with demonstrated expertise in topics such as: Sensor Calibration for IMU, LiDAR, Camera, or RadarStructure-from-motionSLAM or LocalizationSensor fusion3D reconstruction or Multi-view geometry
- Proficiency in C++ and Python programming languages.
- Practical, hands-on experience in constructing a comprehensive sensor calibration stack for autonomous driving systems.
- Profound understanding of autonomous driving sensors, including Camera, LiDAR, Radar, and IMU.
- Experience in developing testable and modular code to maintain a robust production environment.
- The ability to thrive in a fast-paced environment and collaborate effectively across teams and disciplines, supported by excellent interpersonal skills.
- Experience in runtime optimization specifically for Linux and UNIX-like real-time operating systems.
- Experience with embedded CUDA development and associated tooling.
- A proven track record of deploying Machine Learning (ML) models at scale within autonomous driving or related technological fields.
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