## Forvia, a sustainable mobility technology leader
We pioneer technology for mobility experience that matter to people.
## Your mission, roles and responsibilities
Our Interior Radar Development Support Engineer will execute the hands-on work of an in-cabin radar system: flashing, calibration, data collection, data quality, dataset preparation, routine machine learning runs and regression testing. Keep the data and test infrastructure organized so that validation and algorithm teams receive usable data quickly. This role works closely with the radar lead, the validation engineer, the data collection team, algorithm, firmware and tools teams.
## Key Responsibilities
- Flash firmware, ML models and configuration/calibration data onto hardware.
- Execute sensor and antenna calibration in chambers, lab setups and vehicles, including headliner integration
- Run data collection in vehicles, lab setups and calibration chambers against the collection plan
- Check data quality after each session (completeness, label accuracy, synchronization, metadata) and fix issues
- Prepare, check and organize datasets (naming, metadata, index) and deliver them to the algorithm team.
- Run training and evaluation pipelines defined by the algorithm team; run vehicle tuning evaluations for the validation engineer.
- Run regression tests after software, model or configuration updates and report results.
- Write and improve Python scripts for flashing, logging, data conversion, quality checks and regression automation.
- Operate and help improve data collection, visualization and instrumentation tools.
- Reproduce reported issues on the bench and capture logs for root-cause analysis.
- Maintain test setups, fixtures, procedures and calibration documentation.
- Manage test logistics: test subjects, vehicles and chamber booking.
- Support customer demonstrations (setup, execution, data capture).
- Other duties or projects as assigned
- Commitment to the highest ethical standards and adherence to Faurecia’s Code of Conduct.
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## Your profile and competencies to succeed
Qualifications
The ideal candidate will have/be
Minimum education level: Bachelor’s degree in Engineering (electrical, computer, mechatronics, systems, etc.) or equivalent.
Experience
- 5 years of experience in the automotive industry (preferably within automotive electronics) in a similar role.
- 3 years of hands-on experience with electronic hardware, automotive lab equipment and vehicle test benches.
- Experience with machine learning concepts and practical execution; MLOps tools, Docker.
- Experience with MATLAB or similar tools.
Skills and competencies
- Radar or signal processing coursework or projects.
- Camera data handling and sensor synchronization.
- Automotive communication protocols and associated tools knowledge (CAN, Ethernet).
- Familiar with Python pandas and NumPy and running model training / evaluation with PyTorch or TensorFlow.
- Able to work with tools, scripts and datasets.
- Structured, detail-oriented and consistent approach to testing and documentation.
