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Develops motion planning algorithms, collaborates with engineering teams, and optimizes performance for autonomous vehicle decision-making.

Lead development of Avride's control system by architecting and shipping Model Predictive Control (MPC) solutions in modern C++, identifying vehicle dynamics parameters from real-world data, profiling for real-time performance, and providing technical leadership, code review, and mentorship to a small controls team while aligning with Planning and Hardware.

As a Senior Frontend Engineer, you will develop low-latency interfaces for autnomous vehicle control, optimizing for performance and integrating real-time data.

Develop motion planning algorithms for autonomous robots, ensuring safety, smoothness, and reliability in navigation. Collaborate with various teams for improvement and evaluation.

As a Machine Learning Engineer, you will design, train, and deploy ML models for motion planning, develop data pipelines, and integrate models for real-time use in autonomous vehicles.

The role involves designing and developing software for capturing and transmitting vehicle logs, optimizing data storage and processing in the cloud, and supporting the data platform architecture.

The role involves designing and implementing Avride's core framework for self-driving technology, focusing on performance, reliability, and collaboration with various teams for system improvements.

Join Avride's robot simulation team to develop a hybrid simulation framework, integrating real-world data and synthetic environments for autonomous driving tests.

The ML Engineer will develop motion planning models using ML techniques, simulate interactions for outdoor delivery robots, and collaborate across teams for integration.

The Site Infrastructure Engineer will build and maintain server and network infrastructure for autonomous vehicle operations, ensuring high performance and reliability.