- Location
- Remote US · San Francisco, CA · Dallas, TX · Phoenix, AZ · Pittsburgh, PA · Toronto, ON
- Workplace
- Remote
- Type
- Full-time
- Department
- Systems Engineering and Safety
- Source
- Lever
Description
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. Behavior Verification answers one question: is the driver's on-road behavior good enough to release, and how can we prove it even further? We are seeking a rigorous and decisive systems engineer to build the evidence behind that answer. You will define the scenario and test set content that various Software releases are measured against and that enable the development of new capabilities and features, establish the validity checks that keep those test sets trustworthy, and act as the gatekeeper for what enters the release pipeline. Your work directly informs whether autonomy software is promoted.
You will…
- Design and document the scenario and test sets that establish on-road behavior performance, from minimum to comprehensive.
- Own what enters the release pipeline: define and run the scenario validity checks, and approve or refuse test sets before they are used to judge a release.
- Maintain continuous quality feedback of the artifacts test sets depend on — scenarios, taggers, graders and the taxonomy behind them.
- Turn behavior requirements into testable criteria, working with Behavior Requirements on targets and acceptance criteria that someone will sign.
- Investigate behavior deficiencies: find further instances, establish what exposure we have to them, and return them to the pipeline as test escapes.
- Distinguish genuine behavior failures from simulation artifacts, and raise simulation fidelity gaps with the corresponding teams.
Work across the organization — motion planning, simulation, evaluation, operations, safety methodology and platform — and hold the interfaces between them. - Align verification phases and cadence with the organization: what runs on a daily release, and what a significant step in exposure requires.
Qualifications:
- Bachelor’s degree or Master’s degree in a relevant engineering or related field (e.g., Software Engineering, Electrical Engineering, Computer Science, Robotics, Advanced Controls, Aerospace, etc.).
- Experience verifying safety-critical systems, ideallyin autonomous vehicles.
- Demonstrated experience designing test cases from requirements, and assessing what a test set covers and what it misses.
- Applied statistics: sample size, confidence, and what a result does and does not support.
- Proficiency in Python and the ability to navigate a large codebase.
- Familiarity with autonomous vehicle requirements, behaviors and design methodologies.
- Strong ability to structure, document and prioritize a large number of test sets and test strategies.
- Ability to work cross-functionally in a diplomatic way, across teams that do not always agree.
- A drive for commitment and accountability, for yourself and for others.
Bonus/nice to have:
- Trucking or heavy-vehicle exposure.
- Machine learning experience, particularly model evaluation and false-positive / false-negative trade-offs.
- Exposure to regulatory frameworks: NHTSA, ISO 26262, SOTIF.
- Familiarity with the tooling required to extract coverage and exposure from large driving-log sets.
- Comfortable working in a newly formed team, where the process has to be built rather than followed.
Skills
PythonMachine Learning