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 complex business problems into tractable AI-based solutions

Strong verbal/written communication and collaboration skills, with the ability to effectively convey complex technical concepts to diverse audiences

Product-focused thinking with a proven ability to deliver impactful AI solutions

Nice to Haves:

PhD or Master's degree preferred

Experience with MLOps practices and tools for managing the lifecycle of machine learning models

Publications in top-tier AI/ML conferences or journals

Familiarity with techniques for model interpretability and explainability (XAI)

Awareness of ethical AI principles, bias detection, and mitigation strategies in machine learning models, including experience with or understanding of model guardrails

Compensation and Benefits:

Fair and competitive salary based on skills and experience, and annual performance bonus

Equity may be awarded in the form of Restricted Stock Units (RSUs)

Medical, Dental, Vision and Life Insurance, …