Senior Machine Learning Engineer, Foundation Models

As a Senior Machine Learning Engineer, Foundation Models at Cambridge Mobile Telematics (CMT), you will play a crucial role in designing and developing next-generation AI models to enhance risk assessment, driver engagement, crash detection, and claims processing capabilities. Your work will contribute to making roads safer and drivers better. Here is a detailed overview of your role and responsibilities: Role Overview: You will be a part of the DriveWell Atlas team, where you will work on building novel AI models using telematics data. This position offers the unique opportunity to be at the forefront of cutting-edge AI research and its practical applications in the telematics industry. Key Responsibilities: - Contribute to the design, training, and deployment of innovative AI models for telematics applications - Develop algorithms to model physical phenomena related to vehicle and human movement - Implement self-supervised learning methods for multi-modal telematics sensor data - Fine-tune pretrained models for crash detection, driver risk scoring, and claims processing - Build robust models resilient to noise, missing data, and real-world conditions - Collaborate with teams to integrate AI models into production systems - Develop scalable training and inference pipelines using frameworks such as Ray, PyTorch DDP, or Horovod - Optimize models for efficient deployment on cloud and edge/mobile environments - Stay updated on AI/ML research and apply new techniques to telematics challenges - Document findings and share knowledge within the AI/ML knowledge base - Support junior team members through code reviews and collaboration Qualifications: - Bachelors degree or equivalent experience in AI, Computer Science, Electrical Engineering, Physics, Mathematics, or Statistics - 4+ years of post-degree experience in AI/ML - Hands-on experience in developing deep learning models, particularly transformers for time-series or multimodal data - Proficiency in Python and data science libraries like Pandas, NumPy, and scikit-learn - Strong experience with PyTorch or TensorFlow for deep learning - Knowledge of distributed training methods, data processing pipelines, and ML infrastructure - Excellent problem-solving skills and ability to translate complex business problems into AI solutions - Strong communication skills for presenting technical concepts effectively Nice to Haves: - Masters or PhD in relevant field - Experience with model interpretability and ethical AI principles - Exposure to MLOps practices and contributions in AI/ML In addition to the above, Cambridge Mobile Telematics offers competitive compensation, benefits, flexible allowances, and a supportive work environment focused on diversity and inclusion. Join CMT to contribute to improving road safety globally and be a part of a culture that values diverse perspectives and backgrounds. As a Senior Machine Learning Engineer, Foundation Models at Cambridge Mobile Telematics (CMT), you will play a crucial role in designing and developing next-generation AI models to enhance risk assessment, driver engagement, crash detection, and claims processing capabilities. Your work will contribute to making roads safer and drivers better. Here is a detailed overview of your role and responsibilities: Role Overview: You will be a part of the DriveWell Atlas team, where you will work on building novel AI models using telematics data. This position offers the unique opportunity to be at the forefront of cutting-edge AI research and its practical applications in the telematics industry. Key Responsibilities: - Contribute to the design, training, and deployment of innovative AI models for telematics applications - Develop algorithms to model physical phenomena related to vehicle and human movement - Implement self-supervised learning methods for multi-modal telematics sensor data - Fine-tune pretrained models for crash detection, driver risk scoring, and claims processing - Build robust models resilient to noise, missing data, and real-world conditions - Collaborate with teams to integrate AI models into production systems - Develop scalable training and inference pipelines using frameworks such as Ray, PyTorch DDP, or Horovod - Optimize models for efficient deployment on cloud and edge/mobile environments - Stay updated on AI/ML research and apply new techniques to telematics challenges - Document findings and share knowledge within the AI/ML knowledge base - Support junior team members through code reviews and collaboration Qualifications: - Bachelors degree or equivalent experience in AI, Computer Science, Electrical Engineering, Physics, Mathematics, or Statistics - 4+ years of post-degree experience in AI/ML - Hands-on experience in developing deep learning models, particularly transformers for time-series or multimodal data - Proficiency in Python and data science libraries like Pandas, NumPy, and scikit-learn - Strong experience wi