Machine Learning Intern, Applied Mobility Intelligence
About The Position
Cambridge Mobile Telematics (CMT) is the world’s largest telematics service provider. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell® Fusion, gathers sensor data from millions of IoT devices — including smartphones, proprietary Tags, connected vehicles, dashcams, and third-party devices — and fuses them with contextual data to create a unified view of vehicle and driver behavior. Auto insurers, automakers, commercial mobility companies, and the public sector use insights from CMT’s platform to power risk assessment, safety, claims, and driver improvement programs. Headquartered in Cambridge, MA, with offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, CMT measures and protects tens of millions of drivers across the world every day. CMT is looking for a creative, collaborative, and highly motivated Machine Learning Intern to help develop applied machine learning capabilities using real-world mobility and behavioral data. This internship is ideal for someone who enjoys solving ambiguous problems, building practical ML workflows, working with messy real-world datasets, and translating technical findings into clear recommendations. The intern will work on a 12-week applied ML project involving data exploration, labeling strategy, feature engineering, model development, evaluation, and stakeholder-facing analysis. The ideal candidate has strong Python skills, a solid foundation in machine learning, strong initiative, and the ability to make progress in open-ended problem spaces.
Requirements
- Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Data Science, Machine Learning, Statistics, Engineering, Applied Math, or a related quantitative field
- Strong Python programming skills and comfort working with real-world datasets
- Solid understanding of supervised machine learning, feature engineering, classification metrics, train/test splits, and error analysis
- Experience with common Python data science libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or similar tools
- Strong analytical and problem-solving skills, with a bias toward initiative and ownership
- Clear written and verbal communication skills, including the ability to explain technical findings to non-technical stakeholders
Responsibilities
- Explore real-world mobility datasets and identify useful behavioral, temporal, contextual, and aggregate patterns
- Help define practical data labeling guidelines, quality criteria, and edge-case handling rules
- Build and validate datasets for supervised machine learning experiments
- Develop baseline and improved ML models using Python and standard data science workflows
- Perform feature engineering, model evaluation, error analysis, and iteration
- Assess model performance across relevant data segments and edge cases
- Summarize findings, limitations, risks, and opportunities for technical and product stakeholders
- Produce clear documentation, analysis reports, and final recommendations
Benefits
- Fair and competitive hourly rate based on education level
- Flexible scheduling options depending on role and responsibilities
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Career Level
Intern
Education Level
No Education Listed
