Senior Staff AI Engineer, Edge AI

Sonatus•Sunnyvale, CA

13h•$227,000 - $300,000•Hybrid

About The Position

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. 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, traces, and vehicle internal signals (Ethernet and CAN) to detect and predict the health of different sub-systems and anticipate 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 MLOps and integration of AI models on vehicles expected to be on the road today. Expect to experiment with cutting-edge model architectures and best-class development tools. This is a hybrid role out of our Sunnyvale, CA, where you will be expected to work in our office 3 days a week.

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.
Candidates with a strong Computer Vision (CV) / ADAS track record are highly encouraged to apply!

Nice To Haves

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.

Responsibilities

Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) 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., 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.