Principal Machine Learning Engineer, Foundation Models

We are embarking on a transformative journey with DriveWell Atlas, a groundbreaking initiative to build a family of novel AIs on telematics data. As a Principal Machine Learning Engineer on the DriveWell Atlas team, you will be at the forefront of developing these next-generation AIs. You will lead innovative projects focused on designing, pre-training, fine-tuning, and deploying these AIs. Your work will directly contribute to enhancing our capabilities in risk assessment, driver engagement, and crash and claims processing. This role requires a deep understanding of modern AI methods, machine learning, physics-informed modeling, and experience with sensor data. We are not tweaking existing models for marginal gain. You will have an opportunity to build a first-of-its-kind LLM from petabyte-scale data. CMT is looking for a Principal Machine Learning Engineer, Foundation Models to help us change the world. CMT has helped protect over 65 million drivers and prevent over 126,000 crashes worldwide. We build AI to solve some of the most difficult challenges in mobility — understanding and reducing risk, detecting crashes, and getting people life-saving help. The problems are hard. The impact is real. No matter your role, your work will matter at CMT. Responsibilities: Use independent judgment and discretion to lead the design, pre-training, fine-tuning, and deployment of novel foundation models for vehicle telematics Develop and implement novel algorithms for modeling both automotive physics and human driving behavior Pioneer advanced self-supervised learning techniques, including the design and implementation of innovative tasks tailored to multi-modal telematics sensor data to learn rich representations of movement and driver behavior Develop models robust to noise, missing data, and diverse operating conditions typical of real-world mobile sensor and IoT datasets Build and manage scalable training and inference pipelines using tools like Ray, PyTorch DDP, Horovod, or similar frameworks Integrate these AIs into production systems while ensuring high performance and reliability Optimize these AIs for efficient deployment on various platforms, including cloud and edge/mobile devices Collaborate closely with engineering, product, and research teams to translate cutting-edge research into impactful products and features for the DriveWell Atlas platform Mentor junior scientists and contribute to the broader AI/ML strategy at CMT Stay abreast of the latest AI advancements, evaluating and adopting emerging technologies and methodologies relevant to telematics Contribute to efforts in AI explainability and interpretability Complete any tasks as they arise Qualifications: Bachelor’s degree or equivalent years of experience and/or certification in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, or a related field 7+ years of professional experience in AI/ML 3+ years of hands-on experience developing and deploying foundation models, with a strong portfolio in generative AI for sequential or spatio-temporal data Strong, hands-on experience in building and training time-series transformer architectures for complex sensor fusion and behavioral modeling tasks is required Deep expertise in designing pretraining tasks for self-supervised learning on noisy, real-world sensor data Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, scikit-learn) Extensive experience with deep learning frameworks such as PyTorch (preferred) or TensorFlow for large-scale model training and deployment Solid understanding and practical experience with distributed training techniques and efficient training methodologies for large models Experience building and maintaining large-scale data processing pipelines and machine learning infrastructure using tools like Spark, Airflow, Docker, and cloud platforms (e.g., AWS, GCP, Azure) Excellent problem-solving skills and the ability to translate com