Build and ship production algorithmic systems that improve healthcare quality, access, and cost outcomes. The role combines machine learning, optimization, experimentation, and LLM productionization, requiring at least two years of relevant industry or advanced-degree experience.
158k – 190k/yr
Hybrid2+ YOEML Engineering
About the role
Responsibilities
Ship production algorithmic systems end-to-end, from problem framing to launch and iteration.
Frame messy healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks.
Define metrics to judge solution performance and validate solutions before launch.
Select appropriate approaches, including machine learning, optimization, heuristics, expert systems, or simple rules.
Experiment with novel approaches to improve results.
Produce rigorous, well-validated work and review peers' code and analyses.
Build a deep understanding of the healthcare economy and Garner's role in it.
Design provider-tiering algorithms optimizing geographic access and total-cost-of-care savings.
Fine-tune and productionize an LLM-based primary-care experience, including evaluation, guardrails, and quality monitoring.
Build member-engagement models using claims data and in-app behavior to select effective communication channels and timing.
Requirements
2+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent advanced degree.
Bias toward action and ability to quickly translate ideas into working prototypes.
Strong applied problem-solving skills, including defining effective metrics and delivering measurable improvements.
Solid command of relevant methods and model data.
Strong judgment in selecting statistical models, heuristics, optimization approaches, and simpler algorithmic methods.
Strong communication skills and ability to present work clearly to senior stakeholders.
Commitment to a high-performing, mission-driven team with urgency, accountability, and authentic feedback.
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