Machine Learning Engineer

What you will do

• 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

What you have done

• 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

What gives you an edge

• You’ve been an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup
• You’ve built ML infrastructure in a high-growth or operationally complex environment
• You have depth in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems
• You have a background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering
• You have experience with feature stores, feature pipelines, or production data systems at scale
• You’ve helped interview, hire, mentor, or set the technical bar for ML engineers, platform engineers, or data engineers
• You’ve worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments
• You have experience with security, privacy, governance, or compliance considerations for production ML systems