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Staff Robotics Engineer, 3D Sensing

Wayve

RemoteSunnyvale, CAFull TimeStaff
Sign in to applyVerified
Location
Sunnyvale, CA
Employment
Full Time
Work model
Remote
Level
Staff
Posted

Skills

Python

About this role

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Robotics Team The Robotics team is responsible for bridging the gap from our model to our vehicle platforms, in order to let the model focus on making driving decisions. We aim to provide high quality data to the model for making such decisions, whilst abstracting away platform-specific aspects. We combine specialisms across the robotics domain, blending strong theoretical foundations with solid engineering practice to span the spectrum from research to production. 🧠 Your day-to-day Define and evolve the technical architecture and roadmap for Wayve’s LiDAR and cross-sensor 3D sensing capabilities, aligned with product and vehicle-programme requirements. Lead the development of target-based and targetless LiDAR calibration and registration methods, including LiDAR-to-camera alignment using geometric and learned approaches. Develop robust temporal alignment and motion compensation methods using techniques such as LiDAR odometry, nonlinear least-squares optimisation and factor graphs. Establish evaluation methodologies and productionise reliable, scalable 3D sensing systems that operate across diverse vehicles, environments and sensor configurations. Work with engineering and research teams to deliver end-to-end 3D sensing capabilities, while providing technical leadership and mentorship to other engineers. 🧩 What you’ll be working on: Leading the technical development of our LiDAR and cross-sensor 3D sensing capabilities. You will own the systems used to calibrate, synchronise and align LiDARs with cameras and other vehicle sensors, enabling accurate and reliable 3D data for perception, reconstruction and ground-truth generation. This is a senior individual-contributor role with responsibility for shaping the architecture and technical roadmap for 3D sensing, while remaining deeply involved in algorithm development, implementation, evaluation and deployment. You will solve challenging problems arising from real sensor systems—including imperfect time synchronisation, rolling-shutter cameras and LiDAR sweep patterns—and develop robust methods that operate across diverse vehicles, environments and operating conditions. You will collaborate broadly across Wayve and provide technical leadership and mentorship across the 3D sensing stack. 🙌 You should apply if you have: Essential Significant experience developing robotics or autonomous-driving systems involving LiDAR, cameras and 3D sensor data. Strong experience with target-based or targetless sensor calibration and registration, including LiDAR-to-camera alignment. Strong understanding of 3D geometry, state estimation and optimisation techniques, including nonlinear least-squares methods or factor graphs. Experience accounting for sensor timing and motion effects, such as time synchronisation, LiDAR sweep distortion, rolling-shutter cameras and motion compensation. Strong software-engineering skills in C++ and/or Python, with experience taking robotics algorithms from prototype through evaluation and production deployment. Experience defining evaluation methodologies, datasets and metrics for assessing calibration, registration and 3D sensing quality across real-world conditions. Demonstrated technical leadership, including setting technical direction, solving ambiguous cross-functional problems and mentoring other engineers. Desirable Experience with LiDAR odometry, SLAM or mapping systems, including LiDAR motion compensation and deskewing. Experience with metric-depth foundation models and their application to calibration, registration or 3D reconstruction. Familiarity with learned feature correspondences, cross-modal

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