Staff AI Engineer (Edge AI)

Join Sonatus, a global leader in the automotive industry, as a Staff AI Engineer. In this role, you will lead the development of Edge AI for in-vehicle self-aware health monitoring and prediction. You will build and deploy AI models that analyze continuous data generated in the vehicle, detect and predict the health of different sub-systems, and anticipate failures in real-time. This is a hybrid role based in Sunnyvale, CA, with a focus on cutting-edge model architectures and best-in-class development tools.

• Lead the development of Edge AI for in-vehicle self-aware health monitoring and prediction, including building and deploying AI models.
• Own the end-to-end ML pipeline, from data ingestion and model training to deployment on resource-constrained edge devices and model optimization.
• Collaborate with other developers and AI engineers to integrate AI models into vehicles, experiment with cutting-edge model architectures, and mentor junior engineers.
• Experience with libraries like scikit-learn, tslearn, or statsmodels for anomaly detection on sensor data
• Experience with NLP techniques for textual data parsing, sequence modeling (RNN/GRU), vector store, or lightweight LLMs/SLMs
• Candidates with a strong Computer Vision (CV) / ADAS track record are highly encouraged to apply!
• Proven ability to lead technical projects from concept to production in an ambiguous, fast-paced environment. Ability to communicate with stakeholders and articulate trade-offs
• Deep proficiency with PyTorch or TensorFlow, and experience with inference engines like ONNX, TFLite, or TVM
• Proven experience mentoring junior engineers in software development
• Bachelor’s degree in Computer Science, Electrical Engineering, Software Engineering, or a related field
• 7+ years in Machine Learning Engineering, with 3+ years focused on Edge AI or Embedded Systems
• Experience deploying to Edge environments (e.g., ARM-based), managing memory manually, and working with limited compute resources
• Expert Python (for training) and decent working knowledge of modern C++ (C++14/17 for inference)
• MS/PhD in Computer Science, Engineering, or related fields
• Familiarity with Edge systems and preferably automotive formats (CAN, DBC, UDS, SOME/IP, or MQTT
• Understanding of Linux/QNX kernel logs (dmesg), process states, and OS-level debugging
• Experience with NVIDIA TensorRT, Qualcomm SNPE