Senior Engineer, Autonomy ML Systems

Motional•Boston, MA

18h•Onsite

About The Position

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, transforming transportation to create safer streets and more sustainable mobility options. The Systems Readiness and Performance team serves as the crucial bridge between software development and real-world deployment. This team is responsible for driving system design, verifying and validating the autonomy stack, and defining, measuring, and validating system performance targets. They work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of fully driverless IONIQ 5 robotaxis in Las Vegas. The Autonomy Subsystems team at Motional focuses on designing and evaluating modules of the autonomy software. This role is for a talented senior engineer to contribute to the evaluation of machine learning subsystems. The team performs various evaluations, including offline model evaluation, open and closed-loop re-simulations, and assessment of on-road performance. The primary 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 with them to improve the performance of ML models on the car. The ideal candidate is a highly-technical systems engineer with experience designing and evaluating safety-critical systems and machine learning models, bringing passion for autonomy, robotics, and machine learning to help build a production-ready system and prepare for scaled robotaxi service deployment.

Requirements

An engineer who has demonstrated strong cross-functional collaborative skills and is highly self-starting.
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.

Nice To Haves

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.

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.