Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. They are seeking a talented senior engineer to contribute to the evaluation of machine learning subsystems, focusing on designing and evaluating modules of the autonomy software.
Responsibilities:
- Define, prototype, and validate advanced metrics that bring new insights to model performance evaluation for autonomy subsystems (mainly focused on perception, prediction, and planning)
- Assess coverage of existing training and testing, then curate test sets that sufficiently capture Motional’s intended deployment operational design domain, across many axes (agent types, interaction types, map/spatial, etc.)
- Evaluate machine learning model performance against these metrics and tests
- Assess system-level performance and subsystem contribution to system behavior through large-scale data analysis of on-road and simulation events
- Define performance requirements for autonomy subsystems, and supporting analyses required to establish these targets
- Coordinate subsystem-level evaluation with other system-level testing efforts across the systems team in support of the safety case and safety claims
Requirements:
- An engineer who has demonstrated strong cross-functional collaborative skills and is highly self-starting. This is a highly cooperative role across systems, autonomy, infrastructure, and other parts of the organization
- Proven engineer with 5+ years of experience working on high-tech safety critical systems or robotics, ideally from a systems, robotics, or similar background
- Master's degree in relevant fields (systems, robotics, ML, CS, etc.) or Bachelor's degree plus significant relevant in-field experience
- Skilled Python developer, familiarity with analyzing the large scale data required to train and test ML models
- Experience designing unique metrics that capture key aspects of system behavior and are used to enhance model development
- Experience training machine learning models, familiarity with state-of-the-art ML model architectures
- Deep experience with a specific aspect of machine learning for autonomous robotics (perception, prediction, or planning)
- Knowledge of C++, especially applied to safety critical systems
- Experience with systems verification and validation techniques
- Experience with regression testing updates to already-deployed software in a safety-critical environment
- Experience with automotive standards (E.g: ISO 12207, ISO 26262, ISO 21448)
- Experience with software quality management and assurance activities (E.g: IATF 16949, ISO 9001)
- Thorough understanding of robotics systems including sensors, actuators, mechatronics and software systems
- Work in cross collaboration teams to address problems and find solutions