Safety Engineer Expert – Machine Learning - ISO 26262

• Researching, defining, and pioneering best practices and standards for the verification and validation of ML systems
• Applying STPA and related system-safety techniques to identify unsafe control actions and derive ML-specific safety requirements
• Collaborating closely with World Understanding and Behavior teams to define processes for data quality, model robustness, and Operational Design Domain (ODD)
• Developing and formalizing novel safety arguments and evidence to support production-ready deployment of AI-based ADAS features
• Acting as a thought leader, helping shape the integration of machine learning into safety-critical automotive systems

• Bachelor's or Master's degree in Computer Science, Electrical/Computer Engineering, AI/ML Engineering, or a related discipline
• Strong expertise in functional safety and AI/ML safety challenges, ideally in the context of automotive ADAS and autonomous systems
• Advanced studies (PhD) in AI safety, ML validation, or related research areas are considered a strong asset
• Solid understanding of machine learning model development, validation, and testing, and life-cycle data management
• Practical experience applying STPA, FMEA, or similar system-theoretic hazard analyses to automotive or aerospace systems
• Knowledge of emerging standards and guidelines for ML safety (e.g., ISO 21448 – SOTIF, ISO 8800, UL 4600)
• Ability to bridge the gap between research and practical engineering implementation
• Collaborative mindset, able to work across data, perception, and system safety teams

Advanced studies (PhD) in AI safety, ML validation, or related research areas

• Hybrid Work
• Industry leading medical, dental, and vision Insurance
• Access to a telemedicine service
• RRSP program
• Personal and sick days
• Recreation room with pool table and foosball table