POSITION SUMMARY

HBI Solutions Inc. is seeking a highly motivated AI Engineer to join our collaborative team at Stanford University. The intern will contribute to the development and optimization of cutting-edge foundation models for healthcare applications, leveraging Stanford’s large-scale electronic medical record (EMR) data. The ideal candidate will have strong skills in natural language processing (NLP), deep learning frameworks, and experience working with transformer-based architectures such as BERT, GPT, or related LLMs. This role offers the unique opportunity to work at the intersection of AI, clinical medicine, and translational research.

ESSENTIAL FUNCTIONS

· Contribute to the design, development, and fine-tuning of large language models for EMR data, focusing on tasks such as clinical outcome prediction, patient stratification, and decision support.

· Implement transformer-based architectures (e.g., BERT, GPT, LLaMA) and adapt them to healthcare-specific challenges such as domain adaptation, long-sequence modeling, and multi-modal integration.

· Conduct data preprocessing, cleaning, and representation learning on large-scale EMR datasets, ensuring compliance with privacy and ethical guidelines.

· Develop scalable pipelines for training and evaluating LLMs, leveraging distributed computing and cloud-based platforms when appropriate.

· Benchmark models against state-of-the-art methods, improve performance metrics, and ensure interpretability and robustness in clinical contexts.

· Collaborate with cross-functional teams at Stanford, including clinicians, data scientists, and software engineers, to translate research into practical healthcare solutions.

· Document methods, results, and best practices; contribute to research publications, technical reports, and presentations.

EDUCATION/EXPERIENCE REQUIREMENTS

· Bachelor’s degree or above in Computer Science.

· Hands-on experience in developing and training deep learning models, particularly transformer-based architectures (BERT, GPT, T5, etc.).

· Proficiency with Python and deep learning frameworks such as PyTorch or TensorFlow.

· Prior experience with large-scale model training, fine-tuning, or distributed computing environments is preferred.

REQUIRED COMPETENCIES – KNOWLEDGE, SKILLS, ABILITIES

· Strong problem-solving skills and ability to work with complex, high-dimensional data.

· Experience in handling large-scale datasets, including preprocessing, feature engineering, and evaluation.

· Strong interest in applying AI to healthcare and improving patient outcomes.

· Excellent oral and written communication skills; ability to work independently and collaboratively.

· Passion for innovation and interdisciplinary research.

WORKING ENVIRONMENT/PHYSICAL REQUIREMENTS

This position requires working in a research environment at Stanford University, involving close collaboration with clinicians and researchers. The role will primarily be computational, with potential exposure to sensitive medical data under strict compliance with HIPAA and Stanford IRB regulations.

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