Staff Machine Learning Engineer, Sonatus

• 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
• Proven experience as a Tech Lead or 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), 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

Desirable

• Sonatus is a leader in providing technologies, AI and software to global OEMs to build intelligent software-defined vehicles
• We are looking for a Staff Machine Learning Engineer to lead the development of Edge AI for in-vehicle self-aware health monitoring and prediction
• In this role, you will build and deploy AI models that analyze continuous data generated in the vehicle during the day-to-day operation, including system logs and vehicle internal signals (Ethernet and CAN) to detect and predict bugs, anomalies, and component failures in real-time
• You will own the end-to-end ML pipeline—from data ingestion and model training to deployment on resource-constrained edge devices and model optimization
• You will work in a fast-paced startup environment where your code will directly impact fleet reliability and build the next generation of the self-aware vehicle
• You will be expected to collaborate with other leading developers who have a deep understanding and expertise of vehicle software and systems, and other AI developers working on ML ops and integration of AI models on vehicles expected to be on the road today
• Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM) to process unstructured application logs, kernel traces and multi-modalities
• Integrating 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., voltage spikes, latency jitters), across different modalities with system events to identify root causes
• Port and optimize pytorch/TensorFlow models into production-grade 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 (think MBs, not GBs)
• 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