Machine Learning Engineer (Staff)

Job Summary

Required Qualifications

• •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
• •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
• •Must be able to lift 50 lbs
• •Must be available for weekend shifts
• •Valid driver's license

Desired Qualifications

• •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