7 дней назад
Staff Validation Systems Engineer (Automotive AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Validation Systems Engineer (Automotive AI): Developing comprehensive validation strategies, metrics, and test suites for ADAS functions in the Wayve AI Driver with an accent on simulation, physical testing, and evidence-based evaluation. Focus on defining acceptance criteria, analyzing validation results, and scaling automated testing for learned autonomous driving behaviors.
Location: London, United Kingdom; hybrid working with regular time in the office and workshops
Company
develops the AI Driver, an end-to-end learned autonomous driving system.
What you will do
- Lead validation strategy development and execution across simulated and physical test modalities for ADAS functions.
- Define general-purpose metrics for on-road and simulated driving behavior.
- Establish test coverage requirements and build comprehensive test suites.
- Develop acceptance criteria with Product, Safety, and AV Engineering stakeholders.
- Analyze validation results and report findings to Release and Autonomy Engineering teams.
- Coordinate dependencies across data, simulation, evaluation, product, architecture, and autonomy teams to enable scalable automated validation.
Requirements
- Hands-on experience evaluating and validating complex engineered systems through simulated scenario development, real-world testing, or both.
- Experience with simulated and physical testing environments for autonomous systems and with analyzing and reporting validation results.
- Proficiency in Python for implementing metrics and working with evaluation codebases.
- Experience in the automotive or autonomous vehicle industry.
- Strong analytical, communication, documentation, and evidence-based decision-making skills.
- Degree in Computer Science, Robotics, Aerospace, or a related field.
Nice to have
- Background in machine learning, computer vision, motion planning, or data science.
- Experience with SOTIF, safety validation, and learned or adaptive behaviors.
- Experience with modern AI tools, agentic workflows, or SQL for large-dataset analysis.
- Deep understanding of driving behavior and driving-performance measurement.
Culture & Benefits
- Hybrid working policy combining office and workshop time in London with work from home.
- Regular collaboration across product, safety, architecture, data, simulation, and autonomy disciplines.
- Work focused on advancing autonomous driving technology and bringing the AI Driver to customers globally.
Hiring process
- Candidate CV information is used to pre-fill as much of the application form as possible.
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