Manager, AI/ML Engineering
At a glance
Manager-level AI Engineering role at Lyra Health. Remote, United States · full-time · $161,000–$221,500 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Lead and grow the ML engineering team through regular one-to-ones, performance feedback and clear career development
- Set quarterly priorities for AI/ML work, including clinical program mapping, and connect plans to wider business goals
- Turn clinical needs into production models with product and data science partners, with safety built into the approach
- Review designs and guide ML work from experiments to reliable microservices running on Kubernetes
- Set technical direction through architecture guidance and prototypes while building shared engineering standards
What you bring
- You have managed and scaled strong AI/ML engineering teams working on production systems
- You bring hands-on knowledge of ML systems, including transformers, neural networks and fine-tuning
- You understand the ML development lifecycle, including dataset lineage, automated evaluation and large-scale microservice deployment
- You have designed cloud-native systems on AWS or a comparable provider
- You can set longer-term technical priorities and model governance while also developing engineers and managing their performance
Who this fits
This manager role suits an experienced engineering leader who can balance technical direction with hands-on team development. It is remote within the United States.
From the employer
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
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Exceptional People Leadership: Facilitate technical growth through weekly 1:1s, performance management, and clearly defined career trajectories for machine learning engineers.
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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.
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Cultivate Engineering Culture: Establish an inclusive, high-performance environment by championing knowledge sharing, mentorship, and rigorous technical standards across the ML organization.
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Strategic Technical Influence: Collaborate with Product Management and Data Science leads to transform complex clinical requirements into robust, safety-oriented production models.
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ML SDLC Oversight: Maintain a high technical bar through design reviews, guiding the evolution from experimental research to stable microservices deployed on Kubernetes.
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Multiplicative Technical Vision: Provide leadership through strategic architectural guidance and prototypes, focusing on scaling your team's collective impact and technical reach.
Qualifications
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Proven experience in engineering management, specifically leading and scaling high-performing ML/AI teams within production environments.
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Exceptional People Leadership: Strong ability to develop technical talent, manage performance, and build a collaborative team culture.
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Strategic Mindset: Demonstrated experience balancing long-term technical strategy and model governance with day-to-day people leadership.
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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.
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Operational Excellence: Mastery of the ML SDLC, including dataset lineage, automated evaluation, and deploying microservices at scale.
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Cloud Architecture: Strong experience architecting cloud-native solutions on AWS (or equivalent cloud providers).
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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
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Polyglot Engineering Background: Experience writing high-performance production code in Java or Kotlin.
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Healthcare & Sensitive Data Expertise: Experience architecting AI/ML systems within highly regulated environments (HIPAA compliance, SOC2, handling PHI/PII).
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MLOps / Platform Productization: Experience building internal developer platforms or ML tooling used by dozens of data scientists and engineers.