Overview
Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that
Full job description
Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.
With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai
As a Research Engineer in Sensor Signal Processing, you will be a key contributor to the research and development of Waabi’s signal processing stack for autonomous driving. You will collaborate with our team of world-renowned scientists and engineers to build innovative, practical, and scalable solutions that handle massive amounts of sensor data (camera, LiDAR, radar, and other modalities) with low latency and high reliability. We value original, high-impact ideas and rigorous experimental validation. You will…
- Be part of a multidisciplinary team of scientists and engineers working on a system that turns raw sensor data captured under diverse environments into useful signals for autonomous driving.
- Design and implement novel signal processing techniques for sensor data acquisition, fusion, and filtering.
- Optimize signal processing algorithms for deployment on parallel computing architectures (e.g., CPU, GPU, DSP, and specialized accelerators).
- Collaborate with Waabi’s autonomy and hardware teams to ensure the robustness of the entire system.
- Have the opportunity to make contributions to high-impact research papers submitted to top conferences or journals (e.g., TSP, TIP, ICRA, IROS, CVPR, NeurIPS, SIGGRAPH, HPG). Qualifications:
- Signal Processing Theory and Practice. You have a thorough understanding of the fundamentals of signal processing, both classical (filtering, estimation) and learning-based (image denoising and super-resolution, 2D and 3D segmentation). You know how to apply insights from the underlying mathematics (Toeplitz matrices, spectral bandwidth, M-estimators) to design robust numerical algorithms that operate on data from real-world sensors.
- Real-time and Embedded Systems You have experience working with high-throughput data inputs in latency-sensitive algorithms, all under a limited compute and memory budget.
- Rapid Prototyping and Shipping Production Software. You are comfortable rapidly building proofs of concept in a high level language like Python, Julia, or MATLAB. You are equally comfortable reading and developing production-quality software. Bonus:
- Industry experience in 1D (audio), 2D (image), 3D (point cloud), or 4D (radar) signal processing.
- Experience with numerical algorithms and mathematical optimization: BLAS, CHOLMOD, Gauss-Newton, L-BFGS, linear programming.
- Experience with real-time methods: causal and recursive filters, recurrent neural networks, transformers
- Experience with systems programming: buffer management, asynchronous communication, hardware accelerators.
- Solid knowledge in performance profiling and optimization.
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