Senior Machine Learning Engineer
Core
Design, develop, train, and validate Machine Learning and Deep Learning models for ADAS/Automotive validation use cases, including perception, signal processing, and event detection.
Role type
Senior Machine Learning Engineer (ADAS/Automotive)
Builds
End-to-end ML pipelines, Deep Neural Networks (DNNs, CNNs, RNNs, Transformers), and automated data processing pipelines for sensor, CAN, Ethernet, camera, radar, and vehicle telemetry data.
Domain
Automotive / Autonomous Driving / ADAS
Deliverable
production ML models
Required skills
Python, Deep Learning, Machine Learning, Dataset curation, Feature engineering, Hyperparameter tuning, Distributed training, Statistical learning, Error analysis, Automotive signals/ECUs/CAN/Ethernet fundamentals
Preferred skills
ADAS/Autonomous Driving experience, Perception datasets (camera/radar/lidar), Transformer/Foundation models, MLOps tools (MLflow, W&B, Kubeflow, Azure ML, SageMaker), Model explainability/robustness testing, GPU optimization, C/C++, MATLAB, SIL/HIL environments
Technologies
AWS, Azure, HPC, MLflow, Weights & Biases, Kubeflow, Azure ML, SageMaker, CANoe, CANalyzer
Responsibilities
Design and validate ML models for ADAS; Build end-to-end ML pipelines; Develop and train state-of-the-art architectures; Define training/validation/test datasets; Analyze dataset quality and data drift; Implement distributed training workflows; Perform hyperparameter tuning and error analysis; Collaborate on root-cause analysis; Participate in vehicle testing and data collection; Develop automated data processing pipelines.
Seniority
Senior, hands-on IC
