Senior Systems Engineer, Robotaxi Systems

Role overview This position is part of a Systems Readiness and Performance team that bridges autonomy software development and real-world deployment for a commercial robotaxi service. The engineer will design system-of-systems architectures, define and measure performance targets, and shape the interfaces between the autonomy stack, remote operators, and customer-facing software. The work is central to building the safety case for an upcoming fully driverless commercial launch in Las Vegas.

Responsibilities - Define architecture, requirements, and interfaces for remote operation, fleet orchestration, and mapping systems. - Design processes that improve how autonomy, operators, and customers interact as a continuous feedback loop. - Evaluate system performance using data pipelines, simulation suites, and verification and validation campaigns against key launch metrics. - Serve as a primary point of contact across systems, software, AI, remote operations, simulation, and validation teams to improve perception, prediction, and planning behavior. - Create clear documentation for operations and customer experience workflows and model complex interactions between systems. - Track and integrate new technologies and methodologies relevant to autonomous vehicles and systems engineering.

Requirements - 5+ years of systems engineering experience and a bachelor's degree in systems engineering, computer science, aerospace engineering, robotics, or a related field; a master's degree is a plus. - Strong proficiency in Python and solid understanding of modern software development and production best practices. - Experience with complex system interfaces, interface control documents, and interface design. - Strong analytical skills for system performance evaluation and data analysis. - Proficiency with Git and standard version control workflows. - Excellent communication skills and the ability to work effectively in a fast-paced, agile, CI/CD environment.

Nice to have - Experience with autonomous vehicle sensor data such as LiDAR, camera, or radar. - Background in planning and localization for autonomous vehicles. - Familiarity with simulation environments and AV simulation data. - Experience designing verification and validation strategies and test campaigns, including regression suites. - Exposure to data pipeline and visualization tools such as Databricks or Looker, plus statistical analysis or machine learning techniques.