ADAS Interior Perception Systems Engineer

About the role

We are seeking a statistically fluent systems engineer to define and verify performance targets for interior perception systems. This role exists to define, measure, and continuously improve consistency of feature experience across a diverse customer population.

Responsibilities

• Define quantitative performance targets. Translate feature-level requirements into verifiable perception subsystem KPIs (e.g., detection and false-positive rates, time-to-detect) and drive alignment on those targets with feature owners and senior leadership.
• Characterize system performance across the full range of customer and environmental noise factors (occupant anthropometry, appearance variation, eyewear, lighting, sun load) and develop methodologies to drive consistent feature availability across our customer population.
• Design statistically efficient validation strategies (design of experiments, stratified sampling, sequential testing, resampling-based confidence estimation) that characterize system robustness across interacting noise factors while making efficient use of test time and resources.
• Build application-agnostic performance and validation frameworks that decouple core perception KPIs from vehicle-specific factors such as camera mounting position, interior geometry, and occupant position, enabling reuse across platforms.
• Define real-world usage profiles and exposure models to project bench and track performance into expected field performance for a representative customer population.
• Collaborate with feature systems engineers to define design objectives, functional requirements, and architectural decisions for driver engagement systems.
• Partner directly with vendor technical leads and our hardware team to refine sensor and illuminator packaging requirements and propose system architecture modifications that reduce application sensitivity.
• Represent the interior perception subsystem in cross-functional design reviews and support root cause analysis and statistical characterization of any field issues.

Requirements

Minimum qualifications:

• Bachelor’s Degree in Electrical Engineering, Computer Science, Mathematics/Statistics, or related field
• 3+ years of experience decomposing high-level feature requirements into quantitative subsystem KPIs in automotive, aerospace, defense, medical devices, or a similarly safety-relevant domain
• Demonstrated fluency in applied statistics – hypothesis testing, confidence and tolerance intervals, sample size justification, and reasoning about rare-event rates
• Experience defining or executing validation strategies under finite test budgets
• Strong communication skills – particularly the ability to explain and defend a statistical recommendation to non-statistical stakeholders and drive consensus across cross-functional teams

Qualifications

Preferred qualifications:

• Master’s Degree in Electrical Engineering, Computer Science, Mathematics/Statistics, or related field
• 5+ years of systems engineering experience in a safety-relevant domain
• Experience with ADAS perception systems and performance evaluation (ROC/DET analysis, operating point selection, confusion matrix decomposition by subgroup)
• Experience with driver/occupant monitoring or camera-based biometric systems
• Proficiency in Python (or comparable language) for statistical analysis of large datasets
• Familiarity with naturalistic driving studies, exposure modeling, or reliability and field-return analysis
• Understanding of functional safety (ISO 26262) and SOTIF standards and processes
• Experience with systems modeling and/or requirements management tools (SysML/MagicDraw, Polarion, JAMA, DOORS, etc.) and failure mode analysis techniques
• Familiarity with requirement formats including INCOSE and EARS

Skills

• Statistical Fluency
• Validation Strategies
• Communication Skills
• Applied Statistics
• Driver/Occupant Monitoring
• Python Programming
• Naturalistic Driving Studies
• Functional Safety Standards
• Systems Modeling
• Requirement Management Tools