Tasks:
- * Design multi-agent conflict detection and arbitration mechanisms
- * Develop and deploy in-vehicle AI safety models
- * Monitor AI models and agents for runtime anomalies
- * Optimize AI safety evaluation frameworks and metrics
Perks/Benefits:
Skills/Tech stack required:
[AI Agents] [Anomaly Detection] [Automated testing] [C/C++] [Data Processing] [Deep learning] [Embedded Systems] [Language Models] [Large Language Models] [Linux] [Model Interpretability] [Model Quantization] [Multimodal Models] [Object Detection] [ONNX Runtime] [OOD detection] [Python] [PyTorch] [Robustness Testing] [Semantic Segmentation] [Simulation testing] [TensorFlow] [TensorRT] [Trajectory Prediction] [Uncertainty Estimation]
Educational requirements:
[Master's Degree]
Role(s):
[AI/ML Engineer] [AI Safety Engineer] [AI Security Engineer] [Automotive AI Security Engineer] [Engineer] [ML Engineer] [Safety Engineer] [Security Engineer]