Tech Lead Manager (Machine Learning)

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 autonomously and adapt to changing economic and healthcare landscapes.
• Cross-Functional Leadership: Partner with Product and Design to translate ambiguous patient journey touchpoints into concrete ML problems. You will help to define the "surface area" of where and how machine learning can provide the most leverage within the Cedar Pay ecosystem.

Required Skills and Experience

• Track record of building production ML systems: You have 6+ years of professional experience in machine learning engineering, where you have designed, built, and deployed effective ML-powered personalization systems or recommendation engines at scale for consumer-facing products (e.g., fintech, e-commerce, social media, etc.)
• Strong programming skillset: You bring technical excellence as you contribute hands-on across the full ML lifecycle—building data pipelines for feature engineering, training the core models, and enhancing the MLOps infrastructure required to serve and monitor these insights at scale. Expertise in both Python and SQL is highly preferred.
• Leadership: You have 1+ years of experience managing ML engineers and scientists, and you have led a team to execute on a technical roadmap, from ideation to production. Although we prefer candidates who have previous management experience, candidates with experience in an IC Tech Lead ML role where they had informal or dotted-line management responsibilities will still be considered (and for these candidates, we can craft a tailored growth plan into a formal management role at Cedar).
• Collaboration: You have experience collaborating closely with product leaders and software engineers, understanding that ML systems don’t work in isolation but only in the context of a larger software product.
• ROI-driven mindset: You go beyond training models or engineering systems—you have an inclination to dive into the data to understand the "why." You can articulate how to formulate a business problem as a supervised, unsupervised, or reinforcement learning task.  You possess the judgment to determine when the incremental benefits of a model justify the cost of building and operating it. You are comfortable championing simpler, non-ML solutions when they offer a higher return on investment for the business.
• Machine learning for a mission: The idea of applying your technical skillset to improve the healthcare financial experience is something that excites and inspires you.

Nice-to-have, but not required:

• Experience with agentic personalization: You have built and integrated effective Generative AI features into a personalization system, or you have designed tools, components and modules that help enable the development of safe, high-performance GenAI features.
• Leading the build of software products, beyond the AI/ML components: If your tech leadership skillset crosses over from machine learning engineering to product engineering, you will thrive as a leader in this cross-functional team. 

Compensation Range and Benefits:

• Salary Range*: $195,500 - 247,000
• This role is equity eligible
• This role offers a competitive benefits and wellness package

*Subject to location, experience, and education

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