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®, 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. Companies from personal and commercial auto insurance, automotive, rideshare, smart cities, wireless, financial services, and family safety industries use insights from CMT’s platform to power their risk assessment, safety, claims, and driver improvement programs. Headquartered in Cambridge, MA, with offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, CMT serves millions of people through over 95 programs in 25 countries, including 21 of the top 25 US auto insurers.

We are seeking an organized, team-oriented self-starter to support the Data Science team to optimize our utilization of Databricks, Spark, and AWS services for building large-scale machine learning models. These models are deployed in our backend on Android and iOS mobile devices and AWS services, developed in collaboration with engineers from our platform and mobile teams. Your responsibilities will involve enhancing data ETL pipelines, designing and managing databases for research and development, supporting the testing of machine learning models for product releases, and taking ownership of the software frameworks used for model deployment in production. You will work closely with data scientists, mobile developers, platform engineers, and QA engineers, fostering a collaborative environment. The ideal candidate for this role will have prior experience with large-scale ETL pipelines, building scalable, maintainable, and secure backend code, and a strong understanding of fundamental concepts such as unit testing, automation, and continuous delivery.

Responsibilities:

• Develop scalable machine learning infrastructure, empowering the data science team to strike the right balance between cost and speed
• Create infrastructure that accelerates the delivery of new models, designing and implementing tools to enhance the efficiency of feature engineering, model training, and validation
• Offer expert recommendations on current research to drive scalable and cost-effective solutions
• Establish infrastructure to effectively monitor the performance of machine learning models at scale, including those deployed on mobile devices
• Stay up-to-date with the latest trends in training, deployment, and mobile applications of machine learning
• Architect software solutions across mobile, backend, and research domains, ensuring seamless data collection and transmission to support our models effectively
• Develop comprehensive test plans for the smooth deployment of new models across backend and mobile platforms
• Complete any additional tasks as they arise

Qualifications:

• Bachelor’s degree or equivalent years of experience and/or certification
• 4+ years of relevant working experience, with established experience as a Data or Machine Learning Engineer preferred
• Experience working to support data science teams
• Expertise in Big Data infrastructure and networking software (e.g. Databricks, SageMaker, Spark, S3)
• Expertise in the software development process and ability to code in scripting languages for data manipulation (e.g. Python, Pandas, NumPy, scikit-learn, SQL)
• Hands-on Kubernetes framework experience in management, configuration, deployment, and troubleshooting
• System design and documentation skills, including writing and reviewing code to automate away problems within the squad’s domain
• Proficiency in at least two programming languages, with Python being a must - we work primarily in Python with smatterings of shell scripting
• Good understanding of AWS services
• Demonstrated experience with Linux
• Demonstrated experience with database (RDBMS & NoSql) schema design

Pluses:

• Experience using EC2, Lambda, Dynamo, RDS (PostgreS), S3, Kinesis
• Experience with Docker, NGinX, PostgreSQL, Terraform
• Experience building scalable data processing systems for production environment
• Experience with deep learning frameworks e.g. TensorFlow, Keras, Torch, visualization, and performance
• Ability to navigate code, request data from data sources, and code for bug fixes
• Actively seeks feedback and resolves discrepancies with senior team members
• AWS Certification and/or Kubernetes Certification

Compensation and Benefits:

• Fair and competitive salary based on skills and experience
• Equity 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

Additional Perks:

• Feel great working to improve road safety around the world!
• Join one of our many employee resource groups including Black, AAPI, LGBTQIA+, Women, Book Club, and Health & Wellness
• Extensive wellness, education, and employee assistance programs
• CMT will do all that is possible to support our employees and create a positive and inclusive work environment for all!

Commitment to Diversity and Inclusion:

At CMT, we are intensifying our commitment to providing opportunities and career growth to the underrepresented. We are focused on creating an inclusive work environment that encourages a diversity of backgrounds and thought to produce the best products and services within our industry.

CMT is an equal-opportunity employer and strives to create an inclusive and diverse environment that enriches our employees’ lives in and outside of work. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability state. CMT is headquartered in Cambridge MA. To learn more, visit www.cmtelematics.com and follow us on Twitter @cmtelematics.