Senior Machine Learning Engineer

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🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $161k - $273k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Included Health

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

☁️ SaaS

🤝 B2B

👥 HR Tech

SaaS • B2B • HR Tech

Included Health is a healthcare technology company that delivers personalized, employer- and health-plan-focused primary, urgent, and behavioral health care through a single app and a network of virtual and in-person services. It blends AI-driven tools and human care teams to provide 24/7 care coordination, billing and claims advocacy, second opinions from leading specialists, and mental-health support, with the goal of lowering employer healthcare costs and improving member experience and inclusivity.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• Remote-first culture • 401(k) savings plan through Fidelity • Comprehensive medical, vision, and dental coverage through multiple medical plan options (including disability insurance) • Paid Time Off ("PTO") and Discretionary Time Off (“DTO”) • 12 weeks of 100% Paid Parental leave • Family Building & Compassionate Leave: Fertility coverage, $25,000 for surrogacy/adoption, and paid leave for failed treatments, adoption or pregnancies. • Work-From-Home reimbursement to support team collaboration home office work