Staff Machine Learning Engineer

• We're looking for a Staff Machine Learning Engineer to be Sprinter's first dedicated ML engineering hire and build the production systems that train, deploy, monitor, retrain, and serve machine learning models across the company
• This is a founding, first-of-function role
• You will define the blueprint for how ML moves from prototype to production at Sprinter, including our training and inference pipelines, serving patterns, feature workflows, monitoring, validation, retraining, and model governance practices
• You'll work closely with engineering, data, product, operations, and applied science teams to turn models into reliable systems the company can depend on
• That includes serving predictions through APIs and batch jobs, building clean interfaces between data and product systems, and implementing the observability needed to catch drift, data quality issues, latency problems, cost regressions, and silent model degradation before they impact patients or operations
• Just as importantly, you'll make the foundational calls that every future model and ML engineer will build on: build versus buy, serving architecture, feature paradigms, deployment standards, monitoring expectations, and the guardrails that allow us to move quickly without creating fragile systems
• As the function grows, you will have the opportunity to shape the team, define the technical bar, and help build the ML engineering foundation for Sprinter
• 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
• You decide what the pattern should be and bring the rest of the organization along
• You reach for the simplest system that works, adding complexity only when the value justifies it
• You know what it takes to make a model production-ready and can communicate those requirements 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
• 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

Benefits

• Free lunch everyday and fully-stocked microkitchens and coffee/tea bar
• Team offsites, game hours, tech talks, and design sprints
• Health insurance benefits (PPO / HMO / HSA, 100% of family premiums paid)
• Dental / vision / wellness benefits
• 401k matching
• Unlimited PTO
• Flexible work-from-home policy for work/life balance (4 days in-office, accommodation for occasional WFH days)
• Relocation assistance
• Generous parental leave (4 months for the birthing parent, 3 months for a partner, 100% paid)