Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A rapidly growing healthcare technology organization is reimagining how people access care by bringing healthcare services directly to patients’ homes. The organization combines technology, healthcare operations, and clinical expertise to improve access to preventive and chronic care, particularly for patients who face barriers to visiting traditional healthcare facilities.
Key Responsibilities
- Build and lead the organization’s ML engineering function as its first dedicated ML engineering hire.
- Define the 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 ML systems and engineers will build upon.
- 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, and other production workflows.
- Build effective interfaces between data systems, models, and product systems so machine learning can be consumed safely and reliably.
- Maintain feature pipelines and ensure features remain fresh, accurate, and consistent between training and serving environments.
- 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 organizational standards.
- Partner with engineering, data platform, product, operations, and applied science teams to productionize models and improve technical handoffs.
Required Qualifications
- 8+ years of experience building production software, data systems, ML systems, platform infrastructure, or related technical systems.
- Experience building and owning production machine learning systems across training, serving, features, monitoring, and deployment.
- Demonstrated ability to take machine learning models from prototype or research stages into reliable, production-grade systems.
- Experience building or significantly scaling ML infrastructure, MLOps platforms, model-serving systems, feature pipelines, or related infrastructure.
- Experience designing systems that other engineers, data scientists, analysts, or product teams rely upon.
- Experience making architectural decisions involving ML platform design, serving patterns, feature infrastructure, build-versus-buy strategies, and operational standards.
- Experience with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows.
- Experience implementing monitoring, observability, validation, or alerting for ML systems, data systems, or high-reliability production services.
- Experience creating reproducible workflows across data, features, models, training runs, deployments, or experiments.
- Strong collaboration skills with data science, applied science, data platform, product, operations, or backend engineering teams.
- Experience operating in ambiguous environments where established technical playbooks may not exist.
- Ability to balance speed, simplicity, reliability, privacy, and long-term maintainability when designing production systems.
Preferred Qualifications
- Experience serving as an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup.
- Experience building ML infrastructure in a high-growth or operationally complex environment.
- Deep expertise in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems.
- Background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering.
- Experience working with feature stores, feature pipelines, or production data systems at scale.
- Experience interviewing, hiring, mentoring, or establishing technical standards for ML engineers, platform engineers, or data engineers.
- Experience working with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments.
- Knowledge of security, privacy, governance, or compliance requirements applicable to production ML systems.
Disclaimer
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