Tech Lead Manager (Machine Learning)

Cedar; United States; On-site; USD 180,000 - 270,000; Full time

Job summary

Cedar is seeking a Tech Lead Manager to drive the development of machine learning systems powering Cedar Pay's Personalization Engine. This role blends hands-on ML work with people leadership, owning the lifecycle from modeling to deployment and monitoring.

You will guide a team of ML engineers, ensure scalable, compliant systems, and shape product impact through technically rigorous, yet iterative improvements to patient financial journeys.

Responsibilities

• ML System Ownership: Own the machine learning lifecycle end-to-end for the Personalization Engine, ensuring system reliability, scalability, and model effectiveness.
• Full-Stack ML Execution: Lead and execute engineering work from MLOps pipelines to advanced ML modeling while guiding the two ML engineers on the team.
• Technical Leadership and People Management: Mentor and coach the ML engineers, conduct design reviews, and elevate the team's technical bar.
• Balancing Rigor with a Bias for Action: Build robust production-grade systems that scale with millions of healthcare transactions and enable rapid iteration.
• Feedback Loop Optimization: Improve the feedback loop capturing patient reactions to real-time decisions to continually refine models.

Job description

Our healthcare system is the leading cause of personal bankruptcy in the U.S. Every year, over 50 million Americans suffer adverse financial consequences as a result of seeking care, from lower credit scores to garnished wages. The challenge is only getting worse, as high deductible health plans are the fastest growing plan design in the U.S.

Cedar’s mission is to leverage data science, smart product design and personalization to make healthcare more affordable and accessible. Today, healthcare providers still engage with its consumers in a “one-size-fits-all” approach; and Cedar is excited to leverage consumer best practices to deliver a superior experience.

The Role

We are in search of a Tech Lead Manager to lead development of the machine learning systems that underlie our foundational Personalization Engine within Cedar Pay. This role requires deep expertise in machine learning engineering (from ML modeling to MLOps) and a desire to drive impact through a combination of hands-on technical contributions and people management. Your work will serve as the "decisioning brain" for a vast array of product features that are developed by multiple Cedar squads. Your models will navigate thousands of unique patient variables, economic situations, healthcare-specific intricacies, and behavioral patterns, to ensure every patient journey is optimized for both financial resolution and a positive healthcare financial experience. The robust system that you build and scale will be the intelligent core that powers personalized experiences within Cedar Pay.

Key responsibilities

• ML System Ownership: Serve as key DRI (Directly Responsible Individual) for the ML System that powers the Personalization Engine. You will own the machine learning lifecycle end-to-end, ensuring system reliability, platform scalability, and model effectiveness, in partnership with the engineers on the team.
• Full-Stack ML Execution: Lead and execute engineering work ranging from high-performance MLOps (feature stores, pipelines for model deployment, inference, and monitoring) to sophisticated ML modeling (can include training ensemble models, reinforcement learning, multi-armed bandits, or more), while employing guardrails for compliance and fairness. You will lead by example by making hands-on contributions, and also by guiding and empowering the two ML engineers on your team.
• Technical Leadership and People Management: Act as a force-multiplier for the Personalization Foundations squad. You will conduct rigorous code and design reviews, elevating the bar across the team, contributing to a culture that is committed to technical excellence and product impact. You will serve as direct manager for the machine learning engineers on the squad, mentoring and coaching them to support their professional growth.
• Balancing Rigor with a Bias for Action: With a deep understanding of software engineering principles, you build robust, production-grade systems that can handle the scale of millions of healthcare transactions, while also enabling the ability to rapidly operationalize, iterate on, and improve ML models and approaches as we collect data and generate new insights. You understand that the first approach is never perfect, and that learning is a continuous process; you focus on building for the validated present rather than over-engineering for an unproven future.
• Feedback Loop Optimization: Using your combined skillset in data engineering and ML model design, you will improve and expand upon the feedback loop that captures patient reactions to real-time decisions, ensuring that our models learn