Engineering Manager, AI & ML Infrastructure

Spring Health

full-time

Posted on: 9/24/2025

Origin: • 🇺🇸 United States • California

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💰 $159,100 - $213,565 per year

Mid-LevelSenior

AWSAzureCloudGoogle Cloud PlatformKubernetesPythonTerraform

About the role

Report to the Director of AI & ML on the Data Products group and collaborate to execute the vision for maturing AI/ML infrastructure
Build and scale core AI and ML platforms that empower product teams and accelerate feature delivery
Provide technical leadership across the full AI/ML stack, from model registry, CI/CD, and feature stores to LLM orchestration and observability
Champion AI Trust & Safety by translating principles like clinical norms, fairness, and transparency into technical controls and guardrails
Improve MLOps and LLMOps capabilities: establish monitoring for model performance, latency, and cost; define SLOs; build CI/CD pipelines
Execute on strategy: break down large initiatives into clear, phased roadmaps and partner with product managers on prioritization and trade-offs
Manage stakeholders across Product, Member Experience, and Clinical teams and communicate KPI-focused metrics on platform health, adoption, and developer velocity
Lead a high-performing team: foster psychological safety, hire and retain ML engineering talent, set clear goals and KPIs, and run performance reviews
Coach and mentor engineers, using career ladders to create personalized development plans
Hands-on player-coach: engage in technical details, guide architecture and AI safety decisions, and implement workflows using tools like LangGraph

2-4+ years in a formal engineering management role, with direct experience leading teams of 4+ engineers
History of productionizing successful AI/ML platforms and solutions
1+ years of experience iteratively building AI-empowered tools and ensuring they operate safely and at scale
Hands-on experience with the modern AI stack, including orchestration frameworks like LangGraph, observability tools like LangSmith, and best practices for prompt engineering and building safety guardrails
5+ years of experience in software or machine learning engineering; background as a Senior MLE, SRE, or DevOps Engineer working on ML infrastructure
Hands-on experience building, evaluating, and deploying machine learning models
Strong understanding of cloud services (AWS, GCP, Azure), Kubernetes, IaC (Terraform), and CI/CD systems
Proficient in Python
Demonstrated ability to collaborate with product management and other cross-functional partners in an outcome-driven environment
History of successfully delivering complex, multi-month technical projects
Security and privacy awareness; previous experience in a medical/health records industry is preferred
Ability to work in a hybrid role based in San Francisco, with expectation to be in office 2-3 days a week