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 Staff AI Engineer to join our 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 day-to-day operation, including system logs, traces, and vehicle internal signals such as Ethernet and CAN, to detect and predict the health of different subsystems 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 collaborate with developers specializing in vehicle software, systems, MLOps, and AI model integration for production vehicles. The role includes experimentation with advanced model architectures and modern development tools.

This is a hybrid role based in Sunnyvale, CA, requiring in-office work 3 days per week.

Responsibilities

• Build and train AI Edge models such as Transformers, LLMs, CNNs, LSTMs, and Trees to process unstructured application logs, kernel traces, and multimodal data.
• Integrate ML workflows including cloud-based LLM APIs such as Gemini, OpenAI, and Claude, with emphasis on synthetic data creation.
• Develop algorithms to cluster log patterns and detect software regressions, race conditions, and crash precursors.
• Design supervised and unsupervised learning models such as Autoencoders and Isolation Forests for monitoring time-series data from CAN bus and onboard sensors.
• Correlate signal anomalies across modalities with system events to identify root causes.
• Port and optimize PyTorch and TensorFlow models for execution on CPU/GPU targets or embedded NPUs.
• Apply quantization, pruning, distillation, and memory optimization for constrained edge environments.
• Define strategies for on-device filtering and cloud processing.
• Lead edge ML pipeline architecture and mentor junior engineers.

Requirements

• Bachelor's degree in Computer Science, Electrical Engineering, Software Engineering, or related field.
• 7+ years of Machine Learning Engineering experience with 3+ years focused on Edge AI or Embedded Systems.
• Experience mentoring junior engineers.
• Expert Python skills and working knowledge of modern C++.
• Proficiency with PyTorch or TensorFlow.
• Experience with ONNX, TFLite, or TVM.
• Experience with NLP techniques, sequence modeling, vector stores, or lightweight LLMs.
• Experience with scikit-learn, tslearn, or statsmodels.
• Ability to lead projects in fast-paced environments and communicate technical trade-offs.
• Experience deploying to ARM-based Edge environments with constrained compute resources.

Desired Skills

• MS or PhD in Computer Science, Engineering, or related field.
• Familiarity with automotive protocols and formats such as CAN, DBC, UDS, SOME/IP, or MQTT.
• Understanding of Linux/QNX kernel logs, process states, and OS-level debugging.
• Experience with NVIDIA TensorRT and Qualcomm SNPE.

Benefits & Perks

• Health care plan including Medical, Dental, and Vision.
• Flexible and Dependent Care Expense program.
• 401(k) retirement plan.
• Life insurance.
• Unlimited paid time off and paid holidays.
• Hybrid office work arrangement.
• Complimentary lunches, snacks, and beverages on-site.
• Wellness allowance.
• Phone and Internet reimbursement.
• Computer accessory allowance.

Base Salary Pay Range: $197,500 — $272,000 USD