Valeo is a tech global company, designing breakthrough solutions to reinvent the mobility. We are an automotive supplier partner to automakers and new mobility actors worldwide. Our vision? Invent a greener and more secured mobility, thanks to solutions focusing on intuitive driving and reducing CO2 emissions. We are leader on our businesses, and recognized as one of the largest global innovative companies.

Key Responsibilities:

• Design, train, and deploy machine learning models tailored for resource-constrained edge hardware.

  
  
  

• Apply data science methodologies to perform Exploratory Data Analysis (EDA), statistical modeling, and visualization of complex time-series data from powertrain sensors to inform model architecture.

  
  
  

• Perform feature engineering to clean, process, and structure raw sensor datasets, ensuring they are optimized and ready to be fed into the models.

  
  
  

• Conduct hyperparameter tuning to continuously optimize machine learning models for higher accuracy and peak performance.

  
  
  

• Optimize model inference and runtime predictions to meet strict real-time execution constraints on target edge hardware.

  
  
  

• Develop real-time application logic using C and C++ while leveraging Python for model training and data preprocessing.

  
  
  

• Optimize ML models using techniques such as quantization (e.g., float32 to int8), model pruning, and memory optimization.

  
  
  

• Process, filter (e.g., using Kalman filters), and synchronize noisy data acquired from physical temperature and current sensors.

  
  
  

• Interface with and customize hardware platforms, including Raspberry Pi capabilities, Linux/Raspbian OS, GPIO, and SPI.

  
  
  

• Test, benchmark, and validate the performance of ML estimators against real hardware setups or high-fidelity powertrain simulators.

  
  
  

Requirements:

• Proven experience in feature engineering, dataset preparation, and data pipeline development.

  
  
  

• Solid background in applied data science, statistical analysis, anomaly detection, and data visualization for time-series sensor data. 

  
  
  

• Strong knowledge of hyperparameter tuning methodologies to maximize model accuracy and efficiency.

  
  
  

• Deep understanding of model inference optimization and low-latency runtime prediction behavior.

  
  
  

• Strong programming skills in C, C++, and Python.

  
  
  

• Deep understanding of Raspberry Pi hardware, Linux/Raspbian OS customization, and embedded hardware interfaces (GPIO, SPI).

  
  
  

• Practical experience in model optimization, quantization, and pruning to meet strict hardware constraints.

  
  
  

• Solid background in signal processing and handling noisy data from physical sensors.

  
  
  

• Experience validating ML models against real hardware or advanced simulators.

  
  
  

• Experience in TensorFlow Lite for Microcontrollers & SciKit Learn is a plus

  
  
  

Experience:

• 3+ years of relevant experience in machine learning, edge computing, or embedded software development.

  
  
  

Job:

Organization:

Schedule:

Employee Status:

Job Type:

Job Posting Date:

Join Us !
Being part of our team, you will join:
- one of the largest global innovative companies, with more than 20,000 engineers working in Research & Development
- a multi-cultural environment that values diversity and international collaboration
- more than 100,000 colleagues in 31 countries... which make a lot of opportunity for career growth
- a business highly committed to limiting the environmental impact if its activities and ranked by Corporate Knights as the number one company in the automotive sector in terms of sustainable development
More information on Valeo: https://www.valeo.com