AI Group Manager

Who is Eleos?

Today, more people than ever are speaking publicly about their mental health. Whether it’s ourselves, our friends and family, or even public figures, taking care of behavioral health is no longer a taboo — it’s vital, and it’s only human. At Eleos, we are on a mission to help deliver the world’s most effective behavioral care through data, measurement, and personalization. Simply put, we want to empower clinicians to do the essential work only they can do.

What is this opportunity?

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.

You’ll 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 Eleos platform.

Key Responsibilities

Group Leadership & People Management

• Lead and grow a multidisciplinary team including ML researchers, data scientists, data analysts, and clinical subject matter experts.
• Build a team culture that values collaboration, scientific rigor, clear communication, and pragmatic product impact.
• Support career growth across both technical and clinical expert tracks.
• Lead through leaders: manage a group that includes team leads and senior ICs, fostering a strong leadership culture and scalable execution across subteams.
• Act as the face of AI at Eleos — internally and externally — representing our work in cross-functional forums, executive discussions, industry events, and partner conversations.

AI Strategy & LLM-First Execution

• Own the group’s roadmap across LLM-driven product features, research initiatives, and infrastructure needs.
• Drive the shift toward LLMs as a primary modeling approach, while integrating classical ML where valuable.
• Guide selection, fine-tuning, and deployment of LLMs/MM-LLMs optimized for healthcare-specific use cases like documentation, summarization, decision support, and structured data extraction.
• Champion fit-for-purpose modeling strategies by thoughtfully selecting between LLMs, classical ML algorithms (e.g., tree-based models, clustering), neural networks, or hybrid approaches — aligning each solution to the structure of the data, the problem's complexity, and the expected impact.

Cross-functional Collaboration

• Partner closely with Engineering to ensure robust, scalable, and observable deployment of models.
• Work with Product to define AI-backed features that are grounded in user needs and business impact.
• Collaborate deeply with clinical experts to ensure alignment with real-world behavioral health workflows and clinical safety.

Operational & Delivery Excellence

• Establish and evolve best practices for LLM experimentation, evaluation, versioning, and monitoring in production.
• Work with platform and infrastructure teams to ensure the team’s research can be smoothly translated into reliable services.
• Foster applied research initiatives around agent-based modeling, few-shot learning, and RAG while ensuring clear paths to product integration.
• Evaluate solutions with a clear lens on performance, scalability, and COGS, guiding smart tradeoffs between accuracy, latency, and cost

Innovation with Purpose

• Stay up-to-date with the LLM ecosystem (e.g., open source models, agentic frameworks, healthcare-specific tuning).
• Evaluate emerging tools, techniques, and partnerships that can help Eleos stay ahead in clinical AI.
• Create space for applied research while holding a clear bar for integration into the product.

Requir

• ements7+ years of experience leading applied AI or ML teams, including both research and production func
• tions.Deep experience with large language models and NLP, including prompt engineering, fine-tuning, an
• d 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 capabil
• ities.Technical depth across the ML lifecycle — from experimentation and evaluation to deployment, inference optimization, and A/B t
• estingDeep experience working with engineering and product teams to bring AI models into user-facing pro
• ducts.Excellent communication and stakeholder management s