Tech Lead Manager (Machine Learning)

Required

• 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.