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Principal Data Scientist, AI
Cambridge Mobile Telematics
Cambridge, USA
Full-time
Senior · 7+ years experience
Hybrid
Discovered 1 weeks ago
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Responsibilities
- Lead the design, pre-training, fine-tuning, and deployment of novel AIs for telematics applications.
- Develop and implement novel algorithms that represent physical phenomena associated with vehicle and human movements.
- Pioneer advanced self-supervised learning techniques, including the design and implementation of innovative tasks tailored to multi-modal telematics sensor data to learn rich representations of movement and driver behavior.
- Drive the development of 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.
- Integrate these AIs into production systems while ensuring high performance and reliability.
- 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.
- Mentor junior scientists and contribute to the broader AI/ML strategy at CMT.
- Stay abreast of the latest AI advancements, evaluating and adopting emerging technologies and methodologies relevant to telematics.
- Contribute to efforts in AI explainability and interpretability.
- Complete any tasks as they arise
Qualifications
- PhD or MS in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, or a related field.
- 7+ years of experience in AI/ML.
- 3+ years of hands-on experience developing and deploying foundation models (e.g., BERT, GPT, and/or custom domain-specific transformers), with a strong portfolio in generative AI for sequential or spatio-temporal data.
- Strong, hands-on experience in building and training multi-modal and/or time-series transformer architectures for complex sensor fusion and behavioral modeling tasks is required.
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