Senior Engineer, Autonomy ML Systems

Company Overview

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first‑of‑its‑kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.

The Autonomy Subsystems team at Motional is focused on designing and evaluating modules of the autonomy software. We are seeking a talented senior engineer to contribute to evaluation of our machine learning subsystems. Our team performs a variety of evaluations of machine learning models, including offline model eval, evaluation of open and closed‑loop re‑simulations, and assessment of on‑road performance. Our goal is to establish metrics that describe autonomy subsystem performance, evaluate against those metrics to validate performance, and provide meaningful insight to ML developers and teams, working closely alongside them to improve the performance of ML models on the car.

• 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.

• 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.

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

The work environment characteristics described here represent…