Overview As Principal Engineer, you lead the Ground Truth Regression team to validate ML and software changes for autonomous driving. You drive large-scale data analysis and orchestration frameworks, partnering with perception and ML teams to surface insights and ensure safety. You own the development of scalable tooling, datasets, and dashboards that support root-cause analysis and model evaluation. You shape technical strategy and mentor engineers to deliver high-impact outcomes in a fast-moving environment. This role offers the chance to influence how we validate and deploy ML for real-world driving at scale. Compensation / Benefitsmedical, dental, vision401k with company matchhealth savings accountslife insurancepet insurancediversity and inclusion initiatives ResponsibilitiesLead a multi-skilled engineering team delivering critical software for autonomous vehicle ML model and software development.Advance large-scale data warehousing and analysis initiatives.Own end-to-end data analysis workflows that reveal impacts of model and software changes.Create high-quality datasets for improved ML training and edge-case validation.Apply statistical analysis to assess model performance and generalization.Provide technical leadership and mentorship to foster collaboration and continuous improvement.Manage project timelines, resources, and deliverables to achieve successful outcomes. Key requirementsBachelor's degree in Computer Science, Data Science, or related field; Master's preferredStrong leadership, communication, and problem-solving abilitiesProven experience leading a diverse team supporting a large user baseExperience in machine learning, data mining, and statistical analysisProven experience developing and deploying large-scale cloud frameworksStrong Python programming skillsExperience with large-scale data processing and distributed computingleadershipcommunicationproblem-solvingPythonlarge-scale data processingdistributed computing technologies
Not specified in the original listing.
Not specified in the original listing.
