Software Engineer - Machine Learning (SDV), POWER

Key ResponsibilitiesDesign, 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.RequirementsProven 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 plusExperience3+ years of relevant experience in machine learning, edge computing, or embedded software development.