Staff Machine Learning Engineer

Join Sprinter as a Staff Machine Learning Engineer, the company's first dedicated ML engineering hire. In this founding role, you will define the blueprint for moving ML from prototype to production, including training and inference pipelines, serving patterns, feature workflows, monitoring, validation, retraining, and model governance practices. You will work closely with various teams to turn models into reliable systems and make foundational decisions that future models and ML engineers will build on. As the function grows, you will have the opportunity to shape the team and define the technical bar.

• Definir el plano para la transición de modelos de prototipo a producción en Sprinter, incluyendo los pipelines de entrenamiento e inferencia.
• Diseñar y construir pipelines de entrenamiento y de inferencia de producción que sean confiables, observables y mantenibles.
• Establecer prácticas de gobernanza de modelos, asegurando la reproducibilidad, el versionado y la preparación operativa como estándares de la empresa.
• You should be someone who empowers the teams around you, accelerates time to deployment, and knows what a model needs to be truly production-ready
• This role is ideal for a staff-level, hands-on engineer who thinks in systems, has built ML infrastructure from the ground up, and knows how to right-size solutions for a rapidly growing startup
• Balanced speed, simplicity, reliability, privacy, and long-term maintainability in production systems
• Designed systems that other engineers, data scientists, analysts, or product teams rely on
• Worked with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows
• Created reproducible workflows across data, features, models, training runs, deployments, or experiments
• Spent 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems
• Built monitoring, observability, validation, or alerting for ML systems, data systems, or high-reliability production services
• Built or meaningfully scaled ML infrastructure, MLOps platforms, model-serving systems, feature pipelines, or related infrastructure
• Partnered closely with data science, applied science, data platform, product, operations, or backend engineering teams
• Made architectural decisions around ML platform design, serving patterns, feature infrastructure, build versus buy, and operational standards
• Operated in ambiguous environments where there was no existing playbook and technical decisions had a long half-life
• 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
• You’ve worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments
• You’ve been an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup
• You’ve helped interview, hire, mentor, or set the technical bar for ML engineers, platform engineers, or data engineers
• 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’ve built ML infrastructure in a high-growth or operationally complex environment
• You have experience with feature stores, feature pipelines, or production data systems at scale
• You have experience with security, privacy, governance, or compliance considerations for production ML systems