Machine Learning Engineer
Build and lead the first ML engineering function at Sprinter Health. Design and implement production ML platforms for training, serving, features, monitoring, retraining and governance; productionize models from prototype to reliable systems in a healthcare startup environment.
About the job
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
Skills
MLOps, Model Serving, Feature Pipelines, Training Pipelines, Inference Pipelines, Model Monitoring, Model Governance, Llm Infrastructure, Feature Stores, Cloud Infrastructure, CI/CD, Containers, Kubernetes, Data Pipelines, Observability
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