Job Description
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Company Overview
A technology company focused on healthcare data infrastructure is building systems that connect fragmented medical data sources, transform raw healthcare information into standardized formats, and generate actionable insights for care teams and their technology platforms.
Key Responsibilities
- Build predictive machine learning models using large-scale clinical datasets for applications such as risk stratification, utilization prediction, and care-gap detection.
- Own the complete machine learning lifecycle, including problem framing, feature engineering, model training, evaluation, deployment, monitoring, and retraining.
- Develop models using a range of approaches, including regression, tree-based methods, dimensionality reduction, and deep learning.
- Transform unstructured clinical information such as PDFs, medical images, and physician notes into structured and usable data.
- Build and maintain ML infrastructure, including training and inference pipelines, experiment tracking, model versioning, and evaluation systems.
- Establish reliable processes that make machine learning models measurable, debuggable, and production-ready.
- Monitor deployed models for performance degradation, data drift, and other production issues.
- Work directly with engineering teams, company leadership, and customers to identify and prioritize high-impact machine learning opportunities.
- Develop scalable solutions for processing large volumes of clinical documents with incomplete or inconsistent metadata.
- Build models that can predict healthcare outcomes and generate useful intelligence from patient medical histories.
- Apply LLMs pragmatically when they provide meaningful value while selecting smaller, more efficient, or more reliable models when appropriate.
- Develop cloud-based infrastructure and services supporting ML training, inference, and production operations.
- Contribute to the development of scalable data pipelines that support machine learning applications.
Required Qualifications
- 7+ years of experience building machine learning systems using real-world data at scale.
- Strong understanding of machine learning fundamentals across regression, tree-based methods, feature engineering, dimensionality reduction, and deep learning.
- Demonstrated experience building and training machine learning models rather than solely orchestrating third-party APIs.
- Proven track record of delivering machine learning systems that generate measurable results in production.
- Strong software engineering fundamentals with experience building or operating large-scale cloud-based backend systems.
- Experience with AWS or similar cloud platforms.
- Ability to design and operate training pipelines, model-serving systems, and monitoring infrastructure.
- Strong SQL and data engineering skills, including experience working with large datasets and building data pipelines.
- Strong Python development skills.
- Familiarity with TypeScript, AWS, and modern software engineering practices.
- Experience working with messy, sparse, inconsistent, and high-dimensional datasets.
- Ability to take ownership of machine learning projects from initial problem definition through production deployment and ongoing operation.
- Located in San Francisco or the Bay Area, or willing to relocate.
Preferred Qualifications
- Experience with healthcare data standards and technologies such as FHIR, HIE, IHE, EHR/EMR, NPI, TEFCA, ADT, HL7, HEDIS, RAF, SNOMED, LOINC, or ICD-10.
- Experience working with clinical or healthcare datasets.
- Experience as a founder or as the first or sole machine learning hire at an early-stage startup.
- Experience developing machine learning systems for healthcare applications.
- Experience working in highly autonomous, fast-paced startup environments.
Example Projects
- Develop a model to predict expected hospitalizations for new patients based on their medical history.
- Convert freeform physician notes and scanned documents into structured patient information.
- Build classifiers capable of categorizing large volumes of clinical documents with incomplete metadata.
- Develop predictive models that help identify care gaps and support improved healthcare outcomes.
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