Build and deploy algorithmic systems for high-impact healthcare problems, choosing among machine learning, optimization, heuristics, and hybrid approaches. The role requires 4+ years of relevant industry experience, strong applied problem-solving and evaluation skills, and fluency in modern ML tooling.
236k – 260k/yr
On-site4+ YOEML Engineering
About the role
Responsibilities
Design, develop, deploy, and improve algorithmic systems powering healthcare products.
Own ambiguous, high-stakes problems end to end, from problem framing and objective-function definition through production deployment and iteration.
Translate healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks.
Define metrics and validate solutions before release.
Select appropriate approaches, including machine learning, optimization, heuristics, expert systems, and hybrid methods.
Develop novel solutions and prototypes for complex problems.
Review technical work rigorously and establish quality standards and evaluation tooling.
Build a deep understanding of the healthcare economy and the company's role within it.
Example initiatives
Optimize provider tiering across geographic access and total cost of care.
Fine-tune and productionize an LLM-based primary care experience, including evaluation, guardrails, and quality monitoring.
Build member engagement models using claims and in-app behavior to select effective communication channels and timing.
Requirements
4+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 2+ years with a relevant advanced degree.
PhD preferred.
Strong applied problem-solving skills and ability to define and improve meaningful metrics.
Fluency across data and algorithmic methods.
Strong judgment in selecting statistical models, heuristics, optimization, and simpler algorithmic approaches.
Strong communication skills, including explaining complex algorithmic ideas to senior and external stakeholders and securing cross-team alignment.
Bias toward action and ability to rapidly turn ideas into working prototypes.
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