We are seeking a sharp, execution-focused AI Group Manager to lead growing teams of data scientists, data analysts, and clinical experts. This group is central to our mission transforming rich behavioral health data into meaningful, explainable, and scalable AI-powered capabilities.
Youll be responsible for shaping and executing our AI strategy, with a strong focus on large language models (LLMs) and their deployment in real-world clinical settings. Your team will balance rapid innovation cycles with rigorous delivery of production-grade AI systems embedded into the platform.
Youll be responsible for shaping and executing our AI strategy, with a strong focus on large language models (LLMs) and their deployment in real-world clinical settings. Your team will balance rapid innovation cycles with rigorous delivery of production-grade AI systems embedded into the platform.
Requirements:
7+ years of experience leading applied AI or ML teams, including both research and production functions.
Deep experience with large language models and NLP, including prompt engineering, fine-tuning, and RAG.
Demonstrated success in shipping LLM-based features into production environments at scale.
Familiarity with classical ML and DL methods including gradient-boosted trees, clustering, and deep neural networks (CNNs, RNNs, DNNs) ideally in roles that complement LLM capabilities.
Technical depth across the ML lifecycle from experimentation and evaluation to deployment, inference optimization, and A/B testing
Deep experience working with engineering and product teams to bring AI models into user-facing products.
Excellent communication and stakeholder management skills.
7+ years of experience leading applied AI or ML teams, including both research and production functions.
Deep experience with large language models and NLP, including prompt engineering, fine-tuning, and RAG.
Demonstrated success in shipping LLM-based features into production environments at scale.
Familiarity with classical ML and DL methods including gradient-boosted trees, clustering, and deep neural networks (CNNs, RNNs, DNNs) ideally in roles that complement LLM capabilities.
Technical depth across the ML lifecycle from experimentation and evaluation to deployment, inference optimization, and A/B testing
Deep experience working with engineering and product teams to bring AI models into user-facing products.
Excellent communication and stakeholder management skills.
This position is open to all candidates.
