AI Engineer; Time-Series

Bioscope AI is seeking an AI Engineer (Time-Series) to develop advanced multimodal AI systems that integrate time-series data from wearables and CGM with LLM-based reasoning. The role focuses on longitudinal patient analysis, bridging temporal and linguistic modalities, and deploying solutions that comply with privacy, security, and regulatory standards.

• Design and implement state-of-the-art multimodal AI models that combine time-series data from wearables, CGM, and other sensors with large language models for analysis and reasoning.
• Build systems that analyze and interpret patient health trajectories, identifying patterns, anomalies, and clinically relevant insights from continuous monitoring data.
• Create architectures that effectively merge sequential sensor data with natural language medical records, clinical notes, and knowledge bases.
• Stay at the forefront of multimodal AI research, implementing and adapting the latest techniques in time-series forecasting, representation learning, and transformer-based models.
• Collaborate closely with healthcare professionals to translate clinical needs into technical solutions.
• Deploy robust, scalable AI systems that handle real‑world patient data while addressing privacy, security, and regulatory requirements.

• Time‑series analysis and forecasting from wearable devices.
• Integration of large language models with temporal biomedical data.
• Multimodal representation learning and fusion techniques.
• Anomaly detection and pattern recognition in longitudinal health data.
• Temporal reasoning and causal inference from observational data.
• Signal processing and feature extraction from continuous monitoring devices.

• Master’s degree in Computer Science, Machine Learning, Biomedical Engineering, Statistics, or a related quantitative field (PhD preferred).
• Strong foundation in deep learning frameworks, especially PyTorch.
• Experience with foundation models and large language models (transformers, pre-training, fine-tuning).
• Demonstrated experience with time-series analysis and forecasting methods.
• Experience deploying deep learning models to production environments.
• Proficiency in Python and modern ML development tools.
• Strong understanding of machine learning fundamentals and statistics.
• Ability to read and implement research papers.
• Excellent communication skills and collaborative mindset.

• Publications in top-tier ML/AI conferences:
  NeurIPS, ICML, ICLR, KDD, etc.
• Experience with healthcare data: EHR, wearables.
• Background in signal processing or biosignal analysis.

Location:

Boston, MA.