Large Language Model Internship

Frontera Health is developing a tech-enabled platform to provide pediatric therapies for rural families in need. Our platform leverages AI/ML to establish the foundational video-based data model for early intervention and developmental disorders. By working with parents and caregivers in rural communities, we aim to increase access to care, improve health equity, and deliver personalized treatment for better outcomes.

Frontera is led by a world-class leadership team, well capitalized and backed by premier investors (Lightspeed and Lux), and already have 500+ clients eagerly awaiting our services. As Frontera continues to scale, we will continue to expand services and product offerings to better serve the families and providers in our communities.

Large Learning Model Internship

This internship offers a unique opportunity to contribute to the development of a groundbreaking platform that is transforming behavioral healthcare. You'll work alongside a world-class team of engineers on projects that utilize state-of-the-art deep learning architectures and LLMs.

Project Focus

Retrieval Augmented Generation (RAG): You'll develop code to clean and organize data extracted from research papers. This includes building systems to handle diverse layouts and integrate information from various sources such as (webpages, databases, etc)

Large Language Model Fine-tuning: You'll assist in fine-tuning a large language model for multiple roles within the behavioral health ecosystem, such as Board Certified Behavior Analysts (BCBAs), Behavior Technicians (BTs), and caregivers. This will involve leveraging knowledge distillation techniques to create smaller, specialized models tailored for each role.

Qualifications

Final year of a Bachelor's or Master's program in Mathematics, Computer Science, Machine Learning, Artificial Intelligence, or a related field at a top university.

Strong portfolio showcasing expertise in transformers and video foundation models.

Proficiency in deep learning frameworks like PyTorch or TensorFlow, particularly in using transformers for Natural Language Processing (NLP).

Prior experience with computer vision, video analysis, and text processing is preferred.

Excellent Research and collaboration skills.

Full-time availability for 3-4 months (Monday through Friday; 8 hours per day)

If you enjoy working in a dynamic environment, have a background in Machine Learning and/or Computer Vision, and have strong programming skills, come join our internship program at Frontera Health. You will help solve complex challenges in healthcare while having access to tools, resources, and development opportunities.

Join us in building the future of behavioral healthcare!

Frontera Health, Inc. is committed to creating and maintaining a diverse, equitable, and inclusive workplace where everyone feels valued, respected, and has the opportunity to thrive. We believe that our differences make us stronger and that all employees, regardless of their background, experiences, or abilities, contribute to our success.

We are committed to:

Providing equal employment opportunities to all qualified individuals, without regard to race, color, religion, sex, national origin, disability status, sexual orientation, gender identity or expression, age, genetic information, veteran status, or any other characteristic protected by law.

Fostering a culture of inclusion and belonging where everyone feels valued and respected.

Providing reasonable accommodations to employees with disabilities.

Continuously learning and improving our DE&I practices.

We will achieve this commitment by:

Recruiting and hiring a diverse workforce that reflects the communities we serve.

Creating and maintaining an inclusive work environment that is free from discrimination and harassment.

Actively listening to and addressing the needs and concerns of all employees.

We believe that diversity, equity, and inclusion are essential to our success as a company and to our mission of serving the pediatric behavioral health community. We are committed to continuous improvement in this area and welcome feedback from all employees.