Manager, AI/ML Engineering

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 a strategic leader to spearhead the execution of Lyra's machine learning roadmap. In this capacity, you will scale and mentor a high-impact AI/ML engineering team, ensuring our technical vision translates into production-grade systems that operate with clinical precision and high-availability reliability.

The ideal candidate is an experienced engineering manager who excels at balancing long-term technical strategy with hands-on people leadership. You possess a deep passion for developing technical talent, building robust cross-functional partnerships, and cultivating a culture of operational excellence within an AI-driven organization.

Why Lyra? Collaborative Innovation: Thrive on working alongside brilliant colleagues to solve complex, mission-critical challenges. Social Impact: Are deeply motivated by making a tangible difference and supporting individuals during their most vulnerable moments. Cross-Functional Partnership: Enjoy collaborating with a diverse group of physicians, therapists, data scientists, and product leaders.

Responsibilities

Exceptional People Leadership: Facilitate technical growth through weekly 1:1s, performance management, and clearly defined career trajectories for machine learning engineers. Roadmap & Strategy Ownership: Drive the quarterly planning lifecycle for AI/ML workstreams, prioritizing essential initiatives like clinical program mapping while ensuring alignment with broader business objectives.

Cultivate Engineering Culture: Establish an inclusive, high-performance environment by championing knowledge sharing, mentorship, and rigorous technical standards across the ML organization. Strategic Technical Influence: Collaborate with Product Management and Data Science leads to transform complex clinical requirements into robust, safety-oriented production models.

ML SDLC Oversight: Maintain a high technical bar through design reviews, guiding the evolution from experimental research to stable microservices deployed on Kubernetes. Multiplicative Technical Vision: Provide leadership through strategic architectural guidance and prototypes, focusing on scaling your team's collective impact and technical reach.

Qualifications

Proven experience in engineering management, specifically leading and scaling high-performing ML/AI teams within production environments. Exceptional People Leadership: Strong ability to develop technical talent, manage performance, and build a collaborative team culture. Strategic Mindset: Demonstrated experience balancing long-term technical strategy and model governance with day-to-day people leadership.

Deep ML/AI Domain Expertise: Strong technical foundation in machine learning systems (transformers, neural networks, fine-tuning) with the ability to lead others in these domains. Operational Excellence: Mastery of the ML SDLC, including dataset lineage, automated evaluation, and deploying microservices at scale.

Cloud Architecture: Strong experience architecting cloud-native solutions on AWS (or equivalent cloud providers). Strategic Communication: Exceptional ability to distill highly ambiguous technical problems into clear strategic priorities and influence leadership across engineering, product, and business domain disciplines.

Preferred Qualifications

Polyglot Engineering Background: Experience writing high-performance production code in Java or Kotlin. Healthcare & Sensitive Data Expertise: Experience architecting AI/ML systems within highly regulated environments (HIPAA compliance, SOC2, handling PHI/PII). MLOps / Platform Productization: Experience building internal developer platforms or ML tooling used by dozens of data scientists and engineers.