About the role
About Lyra Health Lyra Health is a leading provider of evidence-based mental health care, serving more than 20 million people globally in partnership with employers and more than 100 million through health plan and partner relationships. The company has delivered more than 15 million sessions of mental health care, published more than 35 peer-reviewed studies, and delivered unmatched outcomes in terms of access, clinical effectiveness, and cost efficiency. Extensive peer-reviewed research confirms Lyra’s transformative care model helps people recover twice as fast and results in a 26% annual reduction in overall healthcare claims costs. Lyra is transforming access to life-changing mental health care through Lyra Empower, the only fully integrated, AI-powered platform combining the highest-quality care and technology solutions.
About
the Role We are looking for an experienced Senior AI/ML Engineer to spearhead the design, development, and deployment of next-generation AI capabilities for our platform. In this role, you won't just train models in a sandbox—you will architect robust, scalable production systems that integrate predictive analytics, Natural Language Processing (NLP), and Generative AI to solve complex, real-world problems. As a senior member of
the team, you will bridge the gap between data science research and production-grade software engineering, while mentoring junior engineers and influencing our AI product roadmap. Lyra is for you if you: Thrive on working with brilliant teammates to solve complex, meaningful problems Are passionate about making a social impact and supporting people at their most challenging moments Enjoy cross-functional collaboration with physicians, therapists, data scientists, data analysts and product managers \n
Responsibilities Design, train, fine-tune, and evaluate deep learning, classical ML, and foundational GenAI models (LLMs, diffusion models) to drive core product features. Build, scale, and maintain robust ML pipelines for continuous training, evaluation, and real-time/batch inference using modern MLOps frameworks. Collaborate with backend and frontend teams to integrate AI models into microservices, ensuring low latency, high availability, and optimal resource utilization. Architect scalable data pipelines for preprocessing, vectorizing, and ingestion of massive structured and unstructured datasets. Implement rigorous evaluation frameworks for model alignment, bias mitigation, guardrailing, and cost/latency optimization (e.g., quantization, distillation). Provide technical leadership, conduct thorough code reviews, and mentor junior/mid-level engineers on best practices in software craftsmanship and ML engineering.