Description

You will lead the design, pre-training, fine-tuning, and deployment of novel AIs for telematics applications.

Responsibilities

• Develop and implement novel algorithms representing physical phenomena from vehicle and human movements.
• Pioneer self-supervised learning techniques using multi-modal telematics sensor data to learn movement and driver behavior representations.
• Build and manage scalable training and inference pipelines using Ray, PyTorch DDP, Horovod, or similar frameworks.
• Integrate AI models into production systems while optimizing them for cloud and edge deployment.
• Collaborate across engineering, product, and research teams to translate research into product features.

Required Skills

• PhD or MS in AI, Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, or related field.
• 7+ years of experience in AI/ML.
• 3+ years developing and deploying foundation models (e.g., BERT, GPT, or custom transformers) for sequential/spatio-temporal data.
• Hands-on experience training multi-modal and/or time-series transformer architectures for sensor fusion.
• Deep expertise in designing pretext tasks for self-supervised learning on noisy sensor data.
• Proficiency in Python and libraries like Pandas and NumPy.
• Extensive experience with deep learning frameworks like PyTorch or TensorFlow for large-scale training.
• Experience with distributed training techniques and efficient large model methodologies.
• Experience building pipelines using Spark, Airflow, Docker, and cloud platforms (AWS, GCP, Azure).

Key Skills

Education

Bachelor's degree