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The Senior Technical Program Manager will oversee software performance evaluations, manage resources, and coordinate between engineering teams to ensure quality metrics are met while addressing risks and issues.

Develop and integrate motion-planning and decision-making algorithms for autonomous driving. Build robust, scalable C++ code and CI/CD pipelines, process and analyze vehicle field data, optimize large data pipelines, lead feature development, and mentor junior engineers.

This role involves managing software performance evaluations for autonomous driving, coordinating teams, tracking metrics, and improving system configurations.

Design, implement, and test onboard and offboard localization software pipelines for autonomous vehicles. Develop sensor processing, fusion, and SLAM for deployment, perform code reviews, mentor junior engineers, and coordinate APIs with mapping and cross-functional teams (perception, planning, controls).

Design and implement C++ software enabling remote human operator control and low-latency sensor/semantic data streaming for autonomous vehicles. Build integration and simulation test suites, apply test-driven development, support on-road testing, collaborate with cross-functional teams, and develop RVA agent training tools and scenarios.

The role involves leading the ML Acceleration team, driving strategies for optimizing ML model training, recruiting and mentoring engineers, and collaborating across teams.

The Director of Prediction and ML Planning leads machine learning teams to advance AI-driven behavior in autonomous vehicles, ensuring technical breakthroughs and organizational growth.

The Machine Learning Systems Engineer will optimize ML training systems, improve data pipelines, and develop GPU kernels to enhance scalability and performance.

Lead the development of the ML Data Service platform, manage engineers, drive technical strategy, and ensure high-quality data for ML use cases.

The Machine Learning Systems Engineer will optimize training systems, focusing on speed and reliability for large-scale distributed model training.