Included Health is looking for a Senior Machine Learning Engineer to build and operate production machine learning systems that improve healthcare outcomes, member experience, and business performance. This role sits at the intersection of machine learning, software engineering, and product development. You will help turn promising models into reliable, observable, and reusable production capabilities, while partnering closely with product, clinical, data science, and data engineering stakeholders
Why this role is exciting
- You will help shape the next generation of ML systems at Included Health.
- You will work on high-impact problems where model quality, system reliability, and product fit all matter.
- You will have the opportunity to influence both the architecture of our ML platform and the way machine learning is applied in real member and clinical workflows.
Responsibilities
Lead the design, deployment, and operation of production machine learning systems for both batch and online use cases, with a deep focus on reliability, scalability, and maintainability.
Build and improve the infrastructure for the ML lifecycle. This includes training pipelines and inference workflows. It also covers model deployment patterns, monitoring, alerting, and automating retraining.
Partner with data scientists, engineers, product managers, and domain stakeholders to translate ambiguous business problems into practical ML solutions with clear validation plans and measurable impact.
Guide the shift from prototype to a robust production system. This includes several tasks. These tasks include model packaging and orchestration. They also involve observability, documentation, and operational guardrails.
Improve developer experience for ML at Included Health by creating reusable patterns, templates, tooling, and documentation that make it easier for other engineers to ship production-grade models.
Design and optimize workflows for model evaluation, monitoring, and performance tuning, including system metrics, business metrics, and model-quality signals.
Build systems that support explainability, auditability, and safe downstream consumption of ML outputs in product and operational workflows.
Work collaboratively with the machine learning, data engineering, and application engineering teams. Define clear interfaces. These should connect data platforms, model pipelines, and product integrations.
Make pragmatic technical tradeoffs across latency, cost, complexity, and model quality, especially in real-world systems with imperfect data and evolving business requirements.
Provide technical leadership and mentorship to other engineers, raising the bar for engineering quality, operational excellence, and product-minded ML development.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
4+ years of experience building and deploying machine learning systems in production environments.
Proficient experience owning the full ML lifecycle, including training, evaluation, deployment, monitoring, and iteration in production.
Experience in designing or working with ML infrastructure. This includes training pipelines. It also includes batch or online inference systems, model registries, deployment workflows, and monitoring or alerting systems.
Deep programming skills in Python and solid experience with modern ML libraries such as PyTorch, scikit-learn, or TensorFlow.
Experience with cloud-based ML platforms and infrastructure, such as AWS SageMaker, Vertex AI, MLflow, or comparable tools.
Proficient SQL and data modeling skills, with experience working with large-scale, messy, real-world datasets.
Robust system design skills, including the ability to evaluate tradeoffs and build systems that are robust, observable, and maintainable over time.
Demonstrated product judgment: able to frame ambiguous problems, validate assumptions, choose sensible success metrics, and push back when a proposed ML solution is not the right tool for the problem.
Strong collaboration and communication skills, with the ability to work successfully across engineering, product, data science, and domain teams.
Experience in healthcare, claims, clinical, or other high-stakes domains is a plus.
