Machine Learning Engineer (Staff)

Job summary

STAFF MACHINE LEARNING ENGINEER ABOUT Spr*** At Spr***, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U. skip preventive or chronic care simply because they can’t get to a doctor’s office.

Benefits

Qualifications

• clearly
• You are an accelerator for applied science, data, product, and engineering teams, not a gatekeeper
• You build interfaces that make models easy to consume and hard to misuse
• You prevent silent degradation before it becomes an incident
• You create standards that help future engineers move faster
• You raise the technical bar for everyone who joins the function after you
• DAY TO DAY
• In this role, you might spend your time:
• Deciding what Sprinter’s serving and feature paradigms should be and writing the design docs behind those decisions
• Hardening a training pipeline or batch-inference workflow
• Productionizing a model handed off from another team
• Debugging a model-serving issue or production data quality problem

Responsibilities

• 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

Degrees

Industry

Company size

Relocation