10 дней назад
Staff Robotics Engineer, 3D Sensing (LiDAR)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Robotics Engineer, 3D Sensing (LiDAR): Building calibration, synchronisation and cross-sensor alignment systems for LiDAR, cameras and vehicle sensors with an accent on 3D geometry, state estimation and robust motion compensation. Focus on developing production-grade sensing methods, solving timing and rolling-shutter challenges, and evaluating systems across diverse vehicles, environments and sensor configurations.
Location: Hybrid role based in the London, UK or Sunnyvale, US office, combining office and work-from-home time.
Company
develops autonomous-driving technology and vehicle perception systems.
What you will do
- Define the architecture and technical roadmap for LiDAR and cross-sensor 3D sensing capabilities.
- Develop target-based and targetless LiDAR calibration and registration methods, including LiDAR-to-camera alignment.
- Build temporal alignment and motion compensation methods using LiDAR odometry, nonlinear least-squares optimisation and factor graphs.
- Establish evaluation methodologies, datasets and metrics for 3D sensing quality.
- Productionise reliable, scalable sensing systems across vehicles, environments and sensor configurations.
- Provide technical leadership and mentorship while collaborating with engineering and research teams.
Requirements
- Significant experience developing robotics or autonomous-driving systems involving LiDAR, cameras and 3D sensor data.
- Strong experience with sensor calibration and registration, including LiDAR-to-camera alignment.
- Strong understanding of 3D geometry, state estimation and optimisation techniques.
- Experience handling sensor timing and motion effects, including LiDAR sweep distortion, rolling-shutter cameras and motion compensation.
- Strong software-engineering skills in C++ and/or Python, including taking algorithms from prototype through production deployment.
- Demonstrated technical leadership, cross-functional problem-solving and mentoring experience.
Nice to have
- Experience with LiDAR odometry, SLAM, mapping, deskewing or motion compensation.
- Experience with metric-depth foundation models, learned feature correspondences, cross-modal registration or Gaussian splatting.
- Experience handling dynamic scenes with object detection, motion segmentation or tracking.
- Familiarity with GTSAM, Ceres Solver or g2o.
Culture & Benefits
- Hybrid working policy combining time in offices and workshops with time working from home.
- In-person collaboration supports innovation, relationships, culture and learning.
Hiring process
- The CV is used to pre-fill as much of the application form as possible.
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