Machine Learning Engineer (Staff)

The role

You will build and lead the ML engineering function as the first dedicated hire. You will design and build production training and inference pipelines, serving patterns, and model governance practices.

What you will do

• Build and lead Sprinter's ML engineering function
• Define the ML platform and deployment paradigm
• Make foundational architecture and tooling decisions
• Design and build production training and inference pipelines
• Package models for deployment and serve predictions via APIs or batch jobs
• Build interfaces between data, models, and product systems
• Maintain feature pipelines
• Implement monitoring for model performance, drift, and data quality
• Automate retraining, validation, deployment, and rollback workflows
• Partner with engineering, data, product, operations, and applied science teams

Required qualifications

• 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems
• Built and owned ML systems in production across training, serving, features, monitoring, and deployment
• Taken models from prototype or research stage into reliable, production-grade systems
• Built or meaningfully scaled ML infrastructure, MLOps platforms, model-serving systems, or feature pipelines
• Designed systems that other engineers, data scientists, analysts, or product teams rely on
• Made architectural decisions around ML platform design, serving patterns, and feature infrastructure
• Worked with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows
• Built monitoring, observability, validation, or alerting for ML systems, data systems, or high-reliability production services
• Created reproducible workflows across data, features, models, training runs, deployments, or experiments
• Partnered closely with data science, applied science, data platform, product, operations, or backend engineering teams
• Operated in ambiguous environments where no existing playbook existed

Nice to have

• Early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup
• Built ML infrastructure in a high-growth or operationally complex environment
• Depth in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems
• Background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering
• Experience with feature stores, feature pipelines, or production data systems at scale
• Helped interview, hire, mentor, or set the technical bar for engineers
• Worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments
• Experience with security, privacy, governance, or compliance considerations for production ML systems

Benefits

• Meaningful pre-IPO equity
• Medical, dental, and vision plans 100% paid for you and your dependents
• Flexible PTO
• 10 paid holidays per year
• 401(k) with match
• 16-week parental leave policy for birthing parent
• 8 weeks parental leave for all other parents
• HSA + FSA contributions
• Life insurance
• Short and long-term disability coverage
• Free daily lunch in-office
• Annual learning stipend

Before you apply

• Recruiter Screen
• Hiring Manager Interview
• Hands-on Technical Assessment
• Onsite Interview
• References

About the company

Sprinter Health aims to reimagine how people access care by bringing it directly to their homes. The company has supported more than 2 million patients across 22 states.