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.
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, matching 401k, short-term & long-term disability and parental leave
- Unlimited Paid Time Off including vacation, sick days & public holidays
- Flexible scheduling and work from home policy depending on role and responsibilities
Base Salary Range- The base salary range for this position is: $177,000 to $221,300. This range is specifically for Cambridge, MA
Additional Perks:- Work on a mission with real impact: crashes prevented, injuries avoided, lives protected around the world
- Join an industry leader - 65 million drivers protected, powering 140+ programs across 25 countries
- Be part of the team inventing the future of mobility and road safety
- Move fast, own outcomes, do work that matters
- High ownership, small teams, and direct access to leadership - no layers between your work and its impact
- Unlimited PTO, flexible scheduling, competitive salary, annual performance bonus, RSUs, and full benefits including medical, dental, vision, and 401k match
- Summer Fridays provide team members with half days to recharge
- Join one of our employee resource groups: Black, AAPI, LGBTQIA+, Women, Book Club, and Health & Wellness
- Comprehensive wellness, education, and employee assistance programs
