Machine Learning Engineer – Staff

Sprinter Health

Staff Machine Learning Engineer building production ML systems for Sprinter Health, improving patient care through innovative technology. Collaborate with various teams to implement reliable solutions.

Posted 7/20/2026full-timeSan Francisco • California • 🇺🇸 United StatesLead💰 $220,000 - $270,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and leading ML engineering functions, with a focus on defining ML platforms, deploying models, and ensuring operational readiness. Proficient in creating reliable production systems, monitoring performance, and collaborating with cross-functional teams to enhance ML workflows.

Highest-signal resume keywords

ML Systems DevelopmentProduction Software EngineeringMLOps Platform DesignCloud Infrastructure ManagementModel Monitoring and Observability

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning SystemsProduction Deployment WorkflowsFeature PipelinesModel GovernanceData Systems EngineeringTraining and Inference PipelinesVersion ControlAutomated RetrainingAPI DevelopmentModel Serving

Soft Skills

LeadershipCollaborationMentoringProblem-SolvingCommunication

Tools & Technologies

Cloud InfrastructureContainersCI/CDOrchestration ToolsData Pipelines

Industry Keywords

ML EngineeringMLOpsModel DeploymentObservabilityData Quality

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Build and lead Sprinter’s ML engineering function as the company’s first dedicated ML engineering hire
Define Sprinter’s ML platform and deployment paradigm across training, serving, features, monitoring, retraining, and governance
Make foundational build-versus-buy, architecture, tooling, and platform decisions that future models and engineers will build on
Design and build production training and inference pipelines that are reliable, observable, and maintainable
Package models for deployment and serve predictions through APIs, batch jobs, or other production workflows
Build clean interfaces between data systems, models, and product systems so ML can be consumed safely and reliably
Maintain feature pipelines and ensure features remain fresh, correct, and consistent between training and serving
Implement monitoring for model performance, drift, data quality, latency, cost, reliability, and production behavior
Prevent training-serving skew, silent degradation, and model regressions before they become production issues
Automate retraining, validation, deployment, rollback, and other production ML workflows where appropriate
Establish reproducibility, versioning, model governance, and operational readiness practices as company defaults
Partner with engineering, data platform, product, operations, and applied science teams to productionize models and improve handoffs
Write design docs, define technical standards, and bring the broader engineering organization along on key ML infrastructure decisions
Set the technical bar for ML engineering by helping interview, mentor, and eventually hire engineers who follow

Requirements

What you’ll need

Spent 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, feature pipelines, or related infrastructure
Designed systems that other engineers, data scientists, analysts, or product teams rely on
Made architectural decisions around ML platform design, serving patterns, feature infrastructure, build versus buy, and operational standards
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 there was no existing playbook and technical decisions had a long half-life
Balanced speed, simplicity, reliability, privacy, and long-term maintainability in production systems

Benefits

Comp & perks

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 for all other parents
HSA + FSA contributions
Life insurance, plus short and long-term disability coverage
Free daily lunch in-office
Annual learning stipend
Relocation assistance