- Lyrahealth
- United States,
- Full-Time
- 11 days ago
- USD 161,000 – USD 221,500
Manager, AI/ML Engineering.
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Manager, AI/ML Engineering: our view in 3 lines...
- The Role:This role leads an AI and machine learning engineering team building production systems for Lyra Health’s AI-powered mental health platform.
- The Person:The person will lead the ML roadmap, manage and mentor engineers, run quarterly planning, review designs, and guide the move from experimental research to production microservices on Kubernetes.
- Requirements:The ideal candidate has experience in engineering management, machine learning systems, transformers, neural networks, fine-tuning, AWS, Kubernetes, dataset lineage, automated evaluation, and HIPAA compliance, SOC2, and handling PHI/PII.
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?
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Collaborative Innovation: Thrive on working alongside brilliant colleagues to solve complex, mission-critical challenges.
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Social Impact: Are deeply motivated by making a tangible difference and supporting individuals during their most vulnerable moments.
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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.

