Job Description
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Company Overview
A technology company focused on healthcare data infrastructure is building systems that connect fragmented healthcare data sources, transform raw clinical information into standardized formats, and make that information usable by care teams and intelligent applications.
About the Role
- Own machine learning initiatives from problem framing and customer discovery through model development, deployment, monitoring, and ongoing improvement.
- Develop machine learning systems using large-scale clinical datasets.
- Apply classical machine learning techniques, gradient-boosted models, and deep learning according to the requirements of each problem.
- Build production-ready machine learning systems and maintain models throughout their lifecycle.
- Establish evaluation, monitoring, retraining, and model-quality processes.
- Work with complex healthcare data that may be sparse, inconsistent, high-dimensional, and difficult to structure.
- Build ML infrastructure supporting training, inference, experimentation, model versioning, and evaluation.
- Collaborate directly with founders, engineers, and customers to identify high-impact machine learning opportunities.
- Use large language models pragmatically where appropriate while selecting smaller, more reliable, and cost-effective models when they provide better results.
- Develop machine learning services and supporting infrastructure using Python, TypeScript, AWS, and SQL.
Key Responsibilities
- Build predictive models using clinical data at scale, including risk stratification, expected utilization prediction, and care-gap detection.
- Own the complete ML lifecycle, including problem definition, feature engineering, training, evaluation, deployment, monitoring, and retraining.
- Engineer features from sparse and high-dimensional clinical datasets.
- Transform unstructured clinical information into structured and usable records.
- Process documents such as PDFs, scanned documents, physician notes, and other unstructured healthcare information.
- Build and maintain ML training and inference pipelines.
- Implement experiment tracking, model versioning, evaluation frameworks, and monitoring systems.
- Develop systems that make machine learning models measurable, debuggable, and reliable in production.
- Design and operate infrastructure for scalable ML workloads.
- Monitor models for drift, degradation, and changes in production performance.
- Collaborate with engineering and product teams to integrate ML capabilities into production systems.
- Help determine which machine learning problems can provide the greatest business and customer impact.
Required Qualifications
- 7+ years of experience building machine learning systems that operate on real-world data at scale.
- Strong understanding of machine learning fundamentals across regression, tree-based methods, feature engineering, dimensionality reduction, and deep learning.
- Hands-on experience building and training machine learning models rather than primarily orchestrating third-party APIs.
- Demonstrated experience delivering machine learning systems and measurable results in production environments.
- Strong software engineering fundamentals and experience building or operating large-scale backend systems in the cloud.
- Experience with AWS or similar cloud platforms.
- Ability to own ML training pipelines, model serving, infrastructure, and monitoring end-to-end.
- Strong SQL and data engineering skills.
- Experience working with large datasets and developing data pipelines that support machine learning systems.
- Strong Python development skills.
- Familiarity with TypeScript and modern cloud-based engineering environments.
- Experience working with messy, sparse, inconsistent, and high-dimensional datasets.
- Ability to work effectively in a highly autonomous and fast-paced environment.
- Located in San Francisco or the broader Bay Area, or willing to relocate.
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