Staff ML Engineer — Build Production ML Platform

Sprinter Health; Menlo Park (CA); Hybrid; USD 210,000 - 320,000; Full time

Job summary

Sprinter Health, a Bay Area company with hybrid offices in San Francisco and Menlo Park, seeks a Staff Machine Learning Engineer to build and scale Sprinter’s production ML systems across training, deployment, monitoring, and governance.

You will define pipelines, serving patterns, feature workflows, and observability, working with engineering, data, product, operations, and applied science teams to deliver reliable ML solutions.

Qualifications

• 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems.
• Experience taking models from prototype to production-grade systems.
• Experience with 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 vs 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.

Responsibilities

• 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.
• Reviewing feature freshness, model performance, drift, latency, or cost.
• Building validation and rollback workflows for model deployments.
• Partnering with product and operations teams to understand how model behavior impacts real-world workflows.
• Interviewing a candidate, mentoring an engineer, or setting a new technical standard for the ML engineering function.
• Setting up interfaces that make models easy to consume and hard to misuse.
• Ensuring reproducibility and governance practices for ML deployments.

Skills

Production software

ML systems

Platform infrastructure

Data pipelines

Backend engineering

CI/CD

Observability

Model governance

Model serving

Cloud infrastructure

Tools

Kubernetes

Docker

CI/CD

Orchestration

Job description

Sprinter Health, a Bay Area company with hybrid offices in San Francisco and Menlo Park, seeks a Staff Machine Learning Engineer to build and scale Sprinter’s production ML systems across training, deployment, monitoring, and governance.

You will define pipelines, serving patterns, feature workflows, and observability, working with engineering, data, product, operations, and applied science teams to deliver reliable ML solutions.