Cambridge Mobile Telematics
Principal Machine Learning Engineer, Foundation Models
US · Principal
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About the role
- Your work will directly contribute to enhancing our capabilities in risk assessment, driver engagement, and crash and claims processing.
- 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.
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
- 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
- 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
- Move fast, own outcomes, do work that matters
- CMT enables partners to measure risk, detect crashes, provide life-saving assistance, and streamline claims.
Requirements
- 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
- Strong, hands-on experience in building and training time-series transformer architectures for complex sensor fusion and behavioral modeling tasks is required
- Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, scikit-learn)
- Solid understanding and practical experience with distributed training techniques and efficient training methodologies for large models
- 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
- Familiarity with techniques for model interpretability and explainability (XAI)
Nice to have
- PhD or Master's degree preferred
Compensation
- The base salary range for this position is: $177,000 to $221,300. This range is specifically for Cambridge, MA
- Unlimited PTO, flexible scheduling, competitive salary, annual performance bonus, RSUs, and full benefits including medical, dental, vision, and 401k match
- To date, CMT's technology has helped prevent over 126,000 crashes worldwide.
- Base Salary Range
- The base salary range for this position is: $177,000 to $221,300.
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
Company info
- Cambridge Mobile Telematics (CMT) is the world's largest telematics and AI company for safer mobility.
- Its mission is to make the world's roads and drivers safer.
- The company's AI-driven platform, DriveWell Fusion®, proactively identifies and reduces driving risk, leading to fewer crashes and injuries.
- To date, CMT's technology has helped prevent over 126,000 crashes worldwide.
- Headquartered in Cambridge, MA, CMT operates globally with offices in Budapest, Hungary; Chennai, India; Seattle, Washington; Tokyo, Japan; and Zagreb, Croatia.
- Learn more at www.cmt.ai.
- Be part of the team inventing the future of mobility and road safety
Equal opportunity
- Commitment to Diversity and Inclusion:
This listing is sourced directly from Cambridge Mobile Telematics's careers page and normalized into a canonical job model.