Systems Engineer - Behavior Verification

Accountabilities • Design comprehensive verification strategies: Develop, structure, and document scenario-based test sets that assess autonomous driving behavior, ranging from minimum release requirements to comprehensive performance evaluations.

• Govern software release readiness: Define and execute scenario validity checks, review test set quality, and approve or reject test sets before they are used to evaluate software releases.

• Maintain test data quality: Establish continuous quality feedback processes for scenarios, taggers, graders, and the taxonomies supporting behavior verification, ensuring that test artifacts remain accurate, consistent, and trustworthy.

• Translate requirements into measurable criteria: Collaborate with Behavior Requirements teams to convert behavioral expectations into clear, testable acceptance criteria, performance targets, and formally agreed verification standards.

• Investigate behavioral deficiencies: Analyze identified failures, search for additional instances, quantify the system's exposure to relevant scenarios, and feed missed issues back into the verification pipeline as test escapes.

• Distinguish software failures from simulation limitations: Determine whether observed behavioral issues represent genuine system deficiencies or simulation artifacts, and communicate simulation fidelity gaps to the appropriate engineering teams.

• Coordinate cross-functional verification activities: Work closely with motion planning, simulation, evaluation, operations, safety methodology, and platform teams to align interfaces, responsibilities, and verification processes.

• Define verification phases and cadence: Establish appropriate testing frequencies and verification requirements for daily software releases, significant exposure increases, and other critical development milestones.

• Strengthen release decision-making: Provide clear, evidence-based assessments of autonomous driving performance to support decisions about software readiness, risk, and release progression.

• Improve verification processes: Identify gaps in test coverage, refine test strategies, and contribute to the development of scalable verification methodologies as the team and technology evolve.

Requirements

• Bachelor's or master's degree in a relevant engineering or technical discipline, such as Software Engineering, Electrical Engineering, Computer Science, Robotics, Advanced Controls, Aerospace Engineering, or a related field.

• Professional experience verifying safety-critical systems, ideally within autonomous vehicles, advanced driver-assistance systems, robotics, or other complex engineering environments.

• Demonstrated experience designing test cases from system requirements and evaluating test coverage, including identifying gaps and limitations in existing test sets.

• Applied statistical knowledge, including sample size selection, confidence assessment, and the interpretation of test results and their limitations.

• Proficiency in Python and the ability to navigate, understand, and contribute to a large and complex codebase.

• Familiarity with autonomous vehicle requirements, driving behaviors, and systems engineering or design methodologies.

• Strong organizational skills and the ability to structure, document, prioritize, and maintain large collections of test sets and verification strategies.

• Excellent analytical and problem-solving abilities, with a rigorous approach to evaluating evidence, identifying failures, and distinguishing genuine behavioral issues from testing or simulation artifacts.

• Strong cross-functional collaboration and communication skills, including the ability to work diplomatically with teams that may have differing priorities or technical perspectives.

• A high degree of ownership, accountability, and commitment to quality, with the confidence to make evidence-based decisions and uphold verification standards.

• Adaptability and a proactive mindset, with the ability to establish processes, resolve ambiguity, and contribute effectively in a developing technical organization.

Preferred qualifications:

• Experience with trucking, heavy vehicles, commercial transportation, or other large-scale autonomous vehicle applications.

• Knowledge of machine learning systems, particularly model evaluation and the trade-offs between false positives and false negatives.

• Familiarity with relevant safety and regulatory frameworks, including NHTSA guidance, ISO 26262, and Safety of the Intended Functionality (SOTIF).

• Experience using tools and methodologies to extract scenario coverage and exposure metrics from large-scale driving log datasets.

• Comfort working within a newly established team where verification processes, standards, and workflows must be developed and refined.

Benefits

• Competitive annual salary: CAD $160,000–$204,000 for the Canadian location, depending on qualifications, experience, and other relevant compensation factors.

• Additional compensation: Eligibility for discretionary equity incentive awards and an annual performance bonus.

• Comprehensive healthcare coverage: Medical, dental, and vision benefits for eligible full-time employees.

• Flexible paid time off: Unlimited vacation policy, subject to applicable company guidelines and responsibilities.

• Flexible working arrangements: Flexible working hours and work-from-home support.

• Collaborative work environment: Opportunities to work alongside experienced engineers and specialists in autonomous driving, AI, robotics, and safety-critical systems.

• Team-building and social activities: Regularly scheduled events and activities held in person, off-site, and virtually.

• Office amenities: Daily drinks, snacks, and catered meals when working from the office.

• Meaningful technical impact: Contribute directly to the development and verification of autonomous transportation technologies designed to improve safety and transform mobility.

• Professional growth: Opportunities to develop expertise in Physical AI, behavioral verification, simulation, safety engineering, and next-generation autonomous systems.

• Inclusive workplace: Commitment to diversity, accessibility, equal opportunity, and a supportive environment where people from all backgrounds can contribute and grow.

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