Strategic AI/ML Engineering Leader

Lyra Health; New York (NY); On-site; USD 161,000 - 222,000; Full time

Benefits offered by this job

Healthcare coverage

Equity in company

401K with match

Monthly tech allowance

Parental leave

Job summary

Lyra Health is seeking an experienced engineering manager to lead and scale its ML/AI engineering team, translating a strategic AI roadmap into production-grade, highly available systems.

The role emphasizes mentoring engineers, driving cross-functional collaboration with Product Management and Data Science, and maintaining rigorous SDLC practices across cloud deployments on AWS and Kubernetes.

Qualifications

• Proven experience in engineering management leading ML/AI teams in production environments.
• Strong ability to develop technical talent, manage performance and build a collaborative team culture.
• Strategic mindset balancing long-term technical strategy with day-to-day leadership.
• Deep ML/AI domain expertise in transformers, neural networks, fine-tuning and related tooling.

Responsibilities

• Lead and mentor a high-impact ML/AI engineering team with weekly 1:1s and career planning.
• Own roadmap and quarterly planning for AI/ML workstreams aligned with business goals.
• Foster an inclusive, high-performance engineering culture with strong technical standards.
• Collaborate with Product Management and Data Science to translate clinical needs into production models.
• Oversee ML SDLC from research to stable microservices deployed on Kubernetes.
• Provide architectural leadership to scale the team's impact and capabilities.

Skills

Engineering management

ML/AI leadership

Strategic thinking

Cross-functional collaboration

Cloud architecture (AWS)

Tools

Kubernetes

AWS

ML tooling

Job description

Lyra Health is seeking an experienced engineering manager to lead and scale its ML/AI engineering team, translating a strategic AI roadmap into production-grade, highly available systems.

The role emphasizes mentoring engineers, driving cross-functional collaboration with Product Management and Data Science, and maintaining rigorous SDLC practices across cloud deployments on AWS and Kubernetes.