Principal Machine Learning Engineer

Cambridge Mobile Telematics•Cambridge, MA

20h•$177,000 - $221,300•Hybrid

About The Position

As a Principal Machine Learning Engineer I, you will design, build, and operate production ML systems at scale. You will turn advanced models for sequential and multi-modal data into reliable products that power risk assessment, engagement, and event detection. You will partner with research, product, and platform teams to deliver measurable business impact across cloud and edge. CMT is looking for a Principal Machine Learning Engineer I 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.

Requirements

Master's degree or equivalent years of experience and/or certification in Data Science, Computer Science, Electrical Engineering, Physics, or Mathematics
7+ years of professional experience in AI/ML
3+ years of hands-on experience developing and deploying deep learning models with a strong portfolio in generative AI for sequential or spatio-temporal data
Strong software engineering skills in Python
At least two years of AWS experience and demonstrated knowledge of common AWS services such as EC2, S3, Lambda, Athena, Redshift, etc
At least 2 years of professional experience with computer languages such as Python, C++, Scala, etc
Strong knowledge of SQL concepts, ability to comprehend and write complex SQL statements
Excellent problem solving, communication, and cross-functional collaboration

Nice To Haves

Ph.D. in a relevant field.
Hands-on experience with time-series or multi-modal architectures for sensor fusion and behavioral modeling
Background in self-supervised learning on real-world sensor data and physics-informed modeling
Experience building large-scale data processing and orchestration: Spark or Beam, Airflow, Kafka or Pub/Sub, Docker, and Kubernetes on AWS, GCP, or Azure
Publications in strong AI or ML venues

Responsibilities

Design, build, and operate production ML systems at scale.
Turn advanced models for sequential and multi-modal data into reliable products that power risk assessment, engagement, and event detection.
Partner with research, product, and platform teams to deliver measurable business impact across cloud and edge.