Senior Staff AI Engineer, Edge AI

Role Summary

Sonatus is a global leader in the automotive industry, providing key technologies that enable intelligent AI-defined vehicles. Our solutions are already on the road with millions of vehicles, and we are quickly expanding our offerings for production-grade AI on the Edge. We are looking for a great Senior Staff AI Engineer to join our seasoned AI team and lead the development of Edge AI for in-vehicle self-aware health monitoring and prediction.

Responsibilities

• Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities.
• Integrate ML flows, including cloud-based LLM APIs (Gemini, OpenAI, Claude), with emphasis on synthetic data creation.
• Develop algorithms to automatically cluster log patterns and detect software regressions, race conditions, or crash precursors.
• Design unsupervised and supervised learning models (e.g., Autoencoders, Isolation Forests) to monitor time-series data from CAN bus and on-board sensors.
• Implement logic to correlate signal anomalies (e.g., ADAS drifts, sensor spikes, latency jitters) across different modalities with system events to identify root causes.
• Port and optimize PyTorch/TensorFlow models into production-grade models for execution on CPU/GPU-bound targets or embedded NPUs.
• Apply quantization, pruning, distillation, and memory optimization to ensure models run within strict RAM/Flash budgets.
• Define the data strategy for on-device filtering: pre-processing on device and decide which data is processed locally versus processed in the cloud.
• Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI.

Requirements

• Bachelor's degree in Computer Science, Electrical Engineering, Software Engineering, or a related field.
• 10+ years in Machine Learning Engineering, with 3+ years focused on Edge AI or Embedded Systems.
• Proven experience mentoring junior engineers in software development.
• Expert Python (for training) and decent working knowledge of modern C++ (C++14/17 for inference).
• Deep proficiency with PyTorch or TensorFlow, and experience with inference engines like ONNX, TFLite, or TVM.
• Experience with NLP techniques for textual data parsing, sequence modeling (RNN/GRU), vector store, or lightweight LLMs/SLMs.
• Experience with libraries like scikit-learn, tslearn, or statsmodels for anomaly detection on sensor data.
• 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.
• Experience deploying to Edge environments (e.g., ARM-based), managing memory manually, and working with limited compute resources.

Desired Skills

• 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.