Software Engineer, Applied Intuition

• A track record of driving technical work across team boundaries: comfortable raising problems with the teams that own them, building agreement on systemic fixes, and following through without formal authority
• 4+ years building and operating production software systems, with strong Python and modern C++
• Demonstrated ownership of a pipeline, platform, or internal tool that other teams depended on
• Hands-on depth with a cloud workflow orchestration framework (Flyte preferred; Airflow, Argo, etc.), plus CI/CD, containerization, and data pipelines over large binary or log data
• Strength in debugging systems from logs when the surface symptom is misleading
• Enough sensor and robotics data literacy to reason about validity: what good IMU/INS, GNSS/RTK, lidar, and camera data looks like, and how to design a check that catches bad data
• Experience with physical systems, sensors, or hardware-in-the-loop
• Automates recurring failures out of existence

Desirable

• Our Localization, Calibration, and Mapping team delivers high-integrity solutions that let autonomous systems navigate with precision in real-time, safety-critical environments — across consumer automotive, trucking, mining, and construction
• As a new team focused on platformization, we offer a rare opportunity to own core work leveraged across the entire organization
• You'll build the self-service platform that turns sensor calibration from an expert-dependent process into production infrastructure: cloud workflow orchestration, automated data-health gates, and tooling every autonomy program can run for itself
• You'll get there by living in the current system first — onboarding new vehicle types, so what you build is shaped by real-world data and telemetry
• We encourage engineers to take ownership of technical and product decisions and to interact closely with users to collect feedback
• Own calibration across a growing multi-platform fleet: onboard new vehicle types, root-cause production failures, and convert what you learn into reusable workflows
• Drive cross-team work that raises the reliability of the entire calibration path — partner with build, hardware, and infrastructure teams to trace failures to their source
• Build and operate cloud workflow orchestration for calibration at fleet scale, from data collection through evaluation and deployment
• Design automated data-health gates that catch bad IMU/INS, GNSS/RTK, lidar, and camera data before it reaches the calibration path
• Ship production-quality tooling that non-expert internal users can run themselves
• Debug across the cloud/vehicle boundary — tracing root causes through orchestration and application logs one day, verifying how a sensor is actually wired the next