ClosedLoop
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About
ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce costs using AI/ML. They offer solutions for Providers & ACOs, Payers, Pharma & Life Sciences, and Digital Health, focusing on predicting individual-level health risks. Their platform includes features for data science automation and a healthcare content library to accelerate deployments and improve predictions.
Platform
Task
Features
• predictive analytics
• healthcare content library
• data science automation
Job Opportunities
Head of AI
ClosedLoop is a healthcare data science platform providing AI/ML solutions for predictive analytics, data science automation, and a healthcare content library.
Benefits:
Competitive Salary
Flexible PTO
Premium Health Benefits
401(k) Plan
Company Equity
Education Requirements:
Advanced Degree in Computer Science, Engineering, or related field
Experience Requirements:
Track record of successfully developing and deploying production-scale AI systems
Experience with training and deployment frameworks for LLMs in production environments
8+ years of Python programming experience focused on AI & ML solutions including bringing these solutions to production
5+ years of experience in NLP
3+ years in a Sr Manager or Director role
Other Requirements:
Must be authorized to work in the United States without the need for sponsorship now or in the future.
Comfortable working from our downtown Austin location full-time.
Responsibilities:
Define, shape, and refine our overall AI roadmap in alignment with company objectives and market demands
Drive the development of state-of-the-art conversational AI systems that serve both patients and healthcare providers, focusing on creating intuitive, empathetic, and clinically relevant interactions
Enhance our LLM Ops and evaluation systems, which assess the performance and safety of our LLMs for various healthcare tasks, and prioritize patient safety
Leverage the latest AI research and technology to enhance our LLMs via RAG and fine-tuning, including RLHF (incorporating feedback from both users and clinicians)
Optimize computational efficiency and cost-effectiveness of LLM deployments
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