AI/ML Quality Engineer

About Woven by Toyota

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society. Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.

Job Summary

We are seeking an experienced AI/ML Quality Engineer to join the Global Safety and Quality team responsible for ensuring that technology deliveries from all Woven by Toyota lines of business meet the safety, security, and quality requirements defined by regulations, international standards and internal Toyota rules. This role serves as the second line of defense AI quality expert, ensuring that industry best practices, rules and standards are efficiently integrated into the development of all AI technology.

Key Responsibilities

• Regularly review and improve Woven by Toyota internal engineering standards with regards to the new standards and regulations that are being released for the AI technology, and align with stakeholders from in/outside of the company.
• Assist engineering teams in defining and implementing tailored engineering processes with the focus on AI technology development.
• Support product integrity teams in defining and managing AI technology quality and safety acceptance metrics and criteria.
• Participate in the identification of risks, hazards and potential failures of the products that contain AI technology, acting as a facilitator and mentor during these activities.
• Guide engineering teams through the evaluation and qualification of the SW tools that are used for the AI technology development or contain AI “under the hood”.
• Perform review and assessments of the work products related to the AI technology and created by the engineering teams, highlighting any gaps and managing findings resolution plans.