AI Engineer; Time-Series

What You'll Do

• Develop Novel Multimodal AI Systems: Design and implement state-of-the-art models that integrate time series data from wearables, CGM, etc. with LLM-based reasoning and analysis
• Longitudinal Patient Analysis: Build systems to analyze and interpret patient health trajectories over time, identifying patterns, anomalies, and clinically relevant insights from continuous monitoring data
• Bridge Temporal and Linguistic Modalities: Create architectures that effectively combine sequential sensor data with natural language medical records, clinical notes, and knowledge bases
• Model Development & Research: Stay at the forefront of multimodal AI research, implementing and adapting the latest techniques in time series forecasting, representation learning, and transformer-based models
• Clinical
  
  Collaboration:
  
  Work closely with healthcare professionals to understand clinical needs and translate them into technical solutions
• Production Systems: Deploy robust, scalable AI systems that handle real-world patient data with appropriate attention to privacy, security, and regulatory requirements

• Time series analysis and forecasting from wearable devices (heart rate, activity, sleep patterns, etc.)
• Integration of LLMs 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 related quantitative field (
  PhD preferred
  )
• Strong foundation in deep learning frameworks (PyTorch)
• Experience with foundation models and Large Language Models (transformers, pre-training, fine-tuning)
• Demonstrated experience with time series analysis and forecasting methods
• Experience putting deep learning models into 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 ability to work collaboratively

• 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