Principal Generative AI Scientist

Lirio is healthcare’s leading AI-driven personalization engine empowering healthcare organizations to better engage and support consumers throughout their journey to better health. Our clients are leveraging Lirio’s proprietary Objective-Driven, Agentic AI Framework to provide cohesive, personalized healthcare experiences for their patients that drive meaningful improvements in health outcomes by assisting each person in navigating and overcoming their own unique challenges to improved health. Lirio’s AI Framework deeply integrates behavioral science principles to initiate and drive sustained behaviors across our clients’ populations, optimizing consumer engagement, closing gaps in care, lowering costs, and measurably improving health outcomes. The Principal Generative AI Scientist is responsible for the design, development, and integration of Generative LLM-based conversational models within the broader Lirio AI architecture. The position requires deep collaboration with the rest of the AI team, as well as our behavioral scientists, to realize novel solutions that leverage and contribute to our unique architecture and learning representations to address the unique challenges of deploying patient-facing conversational tools in healthcare that are both measurably effective and trustworthy.

The Principal Generative AI Scientist will lead the integration of conversational, generative-LLM-based technologies into Lirio’s broader Objective-Driven Multi-Agent Framework.

Core Scientific and Product-Related Responsibilities:

• Lead research initiatives to drive conversational AI model improvements and guide the creation of innovation generative AI-driven solutions and products.
• Work closely with the broader team of AI researchers and engineers to find opportunities to improve our conversational technologies, as well as to optimally integrate them into our objective-driven architecture and overall agentic framework.
• Work with the team to ensure we are fully leveraging conversational feedback in our proprietary behavior-based foundation model.
• Research, develop, and improve techniques to ensure that our uniquely formulated behavioral-science content libraries and knowledgebases can be optimally leveraged in real-time by our language models.
• Align and leverage feedback mechanisms within the Objective-Driven AI framework with alignment feedback for our LLM-based conversational models.
• Work with personalization experts on the broader AI team to leverage personalized embeddings and broader context in patient-facing conversational models.
• Contribute to research on the safety and trustworthiness of medical chatbots.
• Plan and carry out the integration of their scientific research advances within Lirio’s products, while following Lirio’s software engineering processes.
• Oversee the design, build and maintenance of scalable, secure generative AI platforms and core services.
• Contribute to Lirio’s overall mission of improving health outcomes for everyone.

Other Qualifications/Experience

• The ideal candidate will have deep expertise in LLMs, conversational AI, deep learning, transformer architectures, attention mechanisms, and related concepts.
• They will also have practical LLM engineering experience involving model alignment, fine-tuning, RAG, guardrail implementation, agentic communication protocols, etc.
• They will also have sufficient expertise in general machine learning principles and theory to have detailed collaborative discussions with other experts on the team focused on deep reinforcement learning, multi-task learning, Joint Embedding Predictive Architectures (JEPA), optimization, etc.
• They will have demonstrated the ability to contribute to scientific advances through publications and other demonstrations of thought leadership
• The ideal candidate will also have experience operationalizing conversational AI models, as well as experience in the deployment and hosting of agentic models. Experience in deploying AI technologies within the healthcare space is highly desirable, though it is not required.

Education

• ​​PhD in Computer Science, Computer Engineering, Mathematics, or a related field.​
• 7+ years of experience
• ​​The ideal candidate will have an extensive R&D background in machine learning with a particular focus on LLMs and conversational models, including both research experience and real-world engineering experience. ​