Clinical AI Engineering Intern at Lumeris in Massachusetts

Summary: Lumeris is a physician-founded company focused on transforming primary care through advanced AI solutions. As a Clinical AI Engineering Intern, you will contribute to the development of large language models aimed at improving patient outcomes using real-world clinical data. Responsibilities: - Fine-tune and train large language models using longitudinal clinical patient data on GCP infrastructure, with a focus on expanding Tom’s predictive capabilities - Contribute to the design and experimentation of transformer-based architectures and embedding models aimed at predicting specific clinical outcomes - Assist in evaluating model performance against defined prediction targets including 30-day readmissions and time-to-event events - Operate in an experiment-driven development environment — designing tests, iterating on model behavior, tracking results, and documenting findings with scientific rigor - Explore and apply LLM fine-tuning techniques (e.g., LoRA, PEFT, supervised fine-tuning) appropriate to the clinical data and prediction objectives - Contribute to the team’s working knowledge of embedding models, sequence-based architectures, and clinical data preprocessing - Work cross-functionally with ML engineers, clinical informatics specialists, and data scientists in a highly collaborative research environment - Engage with a structured mentorship model — supported by project mentors and a dedicated technical mentor throughout the engagement - Prepare and present a formal project deliverable at the conclusion of the internship, summarizing methodology, outcomes, and recommendations Required Qualifications: - Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Computational Biology, Biomedical Informatics, or a closely related field - Coursework or hands-on experience in machine learning, deep learning, or neural networks - Foundational understanding of how LLMs work, including transformer architectures and model training pipelines - Strong analytical and problem-solving skills with a scientific, experiment-driven mindset - Effective communication skills and ability to work collaboratively in a research-oriented team environment Preferred Qualifications: - Experience with LLM fine-tuning techniques (e.g., LoRA, PEFT, instruction tuning, supervised fine-tuning) - Familiarity with Google Cloud Platform (GCP) or other cloud environments for ML workloads - Experience with Survival Modeling Techniques - Experience with standard medical terminologies like OMOP, FHIR - Background in clinical informatics, bioinformatics, or healthcare data - Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or HuggingFace Transformers - Prior research experience (academic, industry, or self-directed) — particularly in applied ML or healthcare AI - Exposure to embedding models, vector databases, or sequence-based modeling - Graduate-level coursework in NLP, deep learning, or biomedical data science is a strong plus Required Skills: Machine learning, Deep learning, Neural networks, Large language models, Transformer architectures, Model training pipelines, LLM fine-tuning techniques, Google Cloud Platform, Survival modeling techniques, Medical terminologies - OMOP, Medical terminologies - FHIR, Clinical informatics, Bioinformatics, Healthcare data, Python, PyTorch, TensorFlow, HuggingFace Transformers, Embedding models, Sequence-based modeling, Analytical skills Benefits: Medical, Vision and Dental Plans, Tax-Advantage Savings Accounts (FSA & HSA), Life Insurance and Disability Insurance, Paid Time Off (PTO, Sick Time, Paid Leave, Volunteer & Wellness Days), Employee Assistance Program, 401k with company match, Employee Resource Groups, Employee Discount Program, Learning and Development Opportunities, And much more...