Staff Systems Engineer - Safety Methodologies

Title: Staff Systems Engineer - Safety Methodologies Location: Remote US & Canada / Dallas, TX / Phoenix, AZ / Pittsburgh, PA / San Francisco, CA / Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai At the heart of our mission is an unwavering commitment to safety. We are seeking a passionate and experienced safety or systems engineer to spearhead the development and implementation of critical safety framework methods that underpin our driverless autonomy readiness decisions. This is a unique opportunity to shape how Waabi quantitatively ensures and validates the safety of our autonomous trucking solution, working with our highly realistic simulator, real-world data, and cutting-edge generative AI techniques. You will play a pivotal role in creating the evidence for safe operation and leading efforts in a rapidly evolving and groundbreaking field. You will... Inform driverless release testing by developing sample-efficient, high-signal safety datasets across simulation and closed-course track testing. Establish and manage a robust feedback loop from on-road monitoring and safety relevant events to continuously expand and refine test coverage. Validate and optimize safety evaluation pipelines by conducting comparative analyses on core safety frameworks and benchmark criteria to continuously evolve our safety frameworks. Own the creation of clear and structured safety artifacts, ensuring all readiness decisions are transparent and fully traceable to validation evidence. Mentor peers by fostering a culture of technical excellence and driving clear, constructive collaboration between teams. Qualifications: Undergrad required; Masters or PhD within an engineering discipline preferred. 7+ years of automotive, robotics or closely related industry experience. Experience with using simulation and real-world testing to make readiness decisions. Excellent scripting and data analysis skills (Python, SQL), and a solid foundation in statistics. Experience with building a strong safety case for autonomous vehicles. Ability to communicate complex concepts or data in a simple-yet-accurate manner. Collaborative team player who works effectively across functional boundaries, driving evidence-backed decisions. Passionate about autonomy, solving hard problems, and creating innovative solutions. Bonus: Experience in launching a driverless product. Experience implementing software systems components. Experience building software systems from scratch. Experience in machine learning. Proficiency with data mining and advanced statistical analysis.