Principal Engineer Team Lead, Behavior Safety & Metrics

Take a leading technical role in shaping how autonomous driving systems are evaluated, measured, and improved across prediction and planning. You will establish the evaluation strategy and quantitative frameworks that connect individual model performance with real-world vehicle behavior, helping ensure autonomous vehicles operate safely, comfortably, and confidently. Working at the intersection of machine learning, robotics, data science, and systems engineering, you will develop advanced metrics, scalable evaluation tools, and data mining strategies. You will also investigate system limitations, assess dataset coverage, and guide improvements throughout the model development lifecycle. Partnering with Safety, Infrastructure, Operations, Legal, and engineering teams, you will help define the evidence and performance standards needed to advance next-generation autonomous driving capabilities.

• Bachelor's degree in Computer Science, Systems Engineering, Robotics, or a related technical field, with at least 7 years of relevant experience; alternatively, a master's degree or PhD with at least 5 years of experience.

• Strong foundation in probability, statistics, data science, and scientific methods applied to complex multi-agent systems.

• Proficiency in Python, SQL, and production-level data analysis, including statistical analysis and visualization.

• Experience defining performance targets and acceptance criteria for safety, comfort, and assertiveness, and producing evidence to support technical decisions.

• Demonstrated ability to solve complex, cross-disciplinary engineering problems and develop innovative, maintainable solutions.

• Proven experience leading technical initiatives, aligning stakeholders, and driving projects through the development process.

• Working knowledge of hazard and safety analysis methods, such as STPA, HAZOP, FMEA, and hazard analysis and risk assessment.

• Strong communication skills, with the ability to explain complex technical concepts to engineering teams, cross-functional partners, and executive stakeholders.

• Preferred experience applying statistical and machine learning methods to quantify performance, detect regressions, and identify behavioral patterns at scale.

• Deep experience in autonomous robotics prediction and/or planning, including evaluation of machine learning-based systems in production environments.

• Familiarity with public autonomous vehicle datasets and benchmarks, such as Waymo Open Dataset and nuScenes, as well as industry metrics for trajectory forecasting and motion planning.

• Experience building or using large-scale data pipelines.

• Familiarity with SOTIF, ISO 26262, UL 4600, AVSC behavior competencies, and other relevant industry standards is an advantage.