Waabi is pioneering Physical AI – starting with autonomous trucks. Press: [email protected] Business: [email protected]
Hybrid · Toronto
You will design and optimize high-performance algorithms for real-time sensor data processing and fusion in autonomous driving systems. You will collaborate with a multidisciplinary team to implement scalable solutions on target hardware using parallel computing architectures.
Hybrid · Toronto
Design and implement scalable pipelines, tools, and workflows to accelerate the development of autonomous driving technology and generative AI simulators. Manage the end-to-end deployment of data solutions and collaborate with stakeholders to define high-quality labelling criteria.
Hybrid · Toronto
You will architect and develop robust state estimation systems for autonomous vehicles by fusing data from a complex sensor suite. Additionally, you will collaborate with cross-functional teams to implement low-latency algorithms and rigorous evaluation frameworks for real-time localization.
Hybrid · Toronto
You will architect and build scalable, AI-driven mapping infrastructure while collaborating with multidisciplinary teams to create high-definition maps. Additionally, you will design robust data pipelines and real-time systems to support the autonomous vehicle fleet.
Hybrid · Toronto
The role involves working on a cutting-edge offline perception and auto-labelling system using computer vision and machine learning, while managing the end-to-end orchestration of the large-scale auto-labelling training and automation ecosystem. Responsibilities also include architecting and scaling data pipelines using distributed computing frameworks and collaborating on deploying new ML architectures into production.
Hybrid · Toronto
The engineer will design and implement scalable ML data pipelines using real-world driving data and Waabi World simulator to train and evaluate deep learning models for the autonomous driving platform. Key tasks include optimizing data formats, improving data sampling for edge-case discovery, and championing engineering excellence through high-quality code.
Hybrid · Toronto
The role involves developing tooling, infrastructure, and pipelines for complex statistical analyses of driving performance at scale, and implementing metrics to understand model performance and discover key scenarios. Responsibilities also include developing a high-availability query service for low-latency analysis over large datasets from real-world driving and simulation.