Join a dynamic team as a Software Engineer, where you'll build impactful applications and systems in AI-powered healthcare, taking ownership of your projects.
Systems Engineer - Behavior Verification
📜 Description
- Design and document scenario and test sets to measure on-road behavior performance.
- Define and run scenario validity checks, approving or rejecting test sets for the release pipeline.
- Maintain the quality of test-set artifacts, including scenarios, taggers, graders, and taxonomy.
- Translate behavior requirements into testable targets and acceptance criteria.
- Investigate behavior deficiencies, identify additional instances and exposure, and return findings to the pipeline as test escapes.
- Distinguish behavior failures from simulation artifacts and coordinate across engineering, safety, operations, and platform teams.
🛠️ Requirements
- 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.
- Trucking or heavy-vehicle exposure.
✨ Benefits
- Equity awards
- Medical coverage
- Dental coverage
- Vision coverage
- Unlimited vacation
- Flexible hours
- Work-from-home support
- Daily drinks, snacks, and catered meals when in office
Full job description
- 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.
- 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.
- 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.
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