Staff Applied AI​/ML Scientist

About Judi Health

Judi Health is a health technology company providing benefit administration solutions to employers, unions, health plans, and government entities. Judi Health replaces fragmented, outdated systems with the industry's first Unified Claims Processing™ architecture, seamlessly consolidating pharmacy and medical benefit administration on a single, secure platform. By delivering true price transparency, eliminating unnecessary middleman fees, and leveraging advanced AI-powered care delivery, Judi Health helps clients achieve unprecedented operational efficiency and service levels.

Location:

Hybrid 3 days (offices in NYC, Denver, CO and Charlotte, NC area)

Join our mission to infuse cutting-edge AI/ML/GenAI into healthcare as a Staff Applied AI/ML Scientist. We are seeking Artificial Intelligence (AI) specialists to join our team and contribute to the development of Large Language Models (LLMs) aimed at advancing state-of-the‑art for AI-based automation for Healthcare. As a Staff Applied Scientist, you will play a crucial role in developing, improving, and exploring the capabilities of multimodal LLMs to solve complex real‑world problems geared towards building automation and Conversational AI products enabling increased productivity and magical experiences for our customers.

The ideal candidate should be passionate about applying AI/ML science to difficult problems and develop scalable customised solutions. We want people who like working in a collaborative team environment and enjoy creating practical, efficient, and high-performance software that leverages Large Language Models (LLM), Multimodal Language Models(MLM), and other ML models and techniques to build amazing capabilities for our customers, partners, and employees.

Most importantly, we are a mission‑oriented, high‑growth startup, and we are looking for folks that are excited to be part of our journey to make lasting impact towards transforming healthcare.

• Investigate the latest LLM MLM models and evaluate and compare accuracy and efficiency for our in‑domain use cases.
• Collaborate with product managers, engineers, and other stakeholders as a specialist and subject matter expert in Artificial Intelligence and Machine Learning science.
• Develop tools and processes to create data pipelines for ML training and customization
• Finetune LLMs for the healthcare domain on our data.
• Help develop and product ionize machine learning (ML) solutions leveraging our customised models in the fields of Document understanding, Search and QnA, GenAI, Virtual Agents, etc.
• Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
• Participate in code reviews, testing, and quality assurance processes.
• Troubleshoot and resolve technical issues related to AI model, integration, deployment and backend services.
• Document and communicate research and development processes, and architectural decisions.
• Ensure the security and compliance of healthcare data, adhering to HIPAA regulations.
• Solve technical problems of the highest scope and complexity for the team.
• Exert influence on the overall objectives and long‑range goals of the team.
• Drive innovation on the team with a willingness to experiment and to boldly confront problems of immense complexity and scope.
• Actively seek out difficult impediments to our efficiency as a team (“technical debt”), propose and implement solutions that will enable the entire team to iterate faster.
• Responsible for adherence to the Capital Rx Code of Conduct including the reporting of non‑compliance.

• Bachelor’s, Master’s degree or higher in Computer Science, Machine Learning, or a related quantitative field;
  PhD preferred
• Minimum 8 years of experience in industry with a strong focus on applied AI/ML research and development
• Strong grasp of OOP, Design Patterns, efficient algorithms, and quality software development.
• Solid understanding of Machine Learning fundamentals such as Deep Neural Networks, Transformer-based LLM and MLMs, Boosted or standard decision trees, RAG,…