Onos Health is addressing the 30% of total U.S. healthcare spending that is wasted due to ineffective care and administrative burden caused by misalignment between providers and payers by building an AI-driven healthcare data platform.
Requirements
- 5+ years experience building and deploying machine learning systems in production
- Significant experience working with data pipelines and Python and related data science/ML libraries
- Experience with developing LLM-based systems and integrating them with user-facing features
- Deep understanding of the limitations of using LLMs and the best practices for using them for reliable, consistent, and accurate outputs
- Experience wearing multiple hats as a generalist backend engineer
- Significant experience working with healthcare data and with HIPAA best practices
- Experience with explainable AI and model governance in regulated industries
Responsibilities
- Design, develop and deploy sophisticated ML models to analyze healthcare data and detect anomalies, classify patients according to level-of-care guidelines, and make accurate recommendations
- Develop LLM/NLU systems to process and extract meaningful information from clinical notes and medical documents
- Build data pipelines that scale efficiently while maintaining strict data privacy and security standards
- Establish machine learning & AI best practices, evaluation frameworks, and model governance for future team growth
- Collaborate with backend engineers to integrate AI/ML capabilities seamlessly into the Onos platform
- Build AI/ML pipelines to analyze medical records to streamline clinical assessments and healthcare quality reviews
- Design explainable AI solutions that provide transparency into model decisions for healthcare professionals
Other
- Motivated to meaningfully improve the way healthcare is administered in the United States
- Customer obsessed and motivated to make an impact in the healthcare space
- A collaborative team player with a focus on delivering measurable results
- Hybrid role based in San Francisco, where you'll be expected to work at our office in person 2-3 times a week.
- Significant responsibility and autonomy from day one
