Staff Software Engineer (AI)
Staff AI engineer at Onos Health building LLM/NLU systems that process clinical notes and classify patients' level of care, plus the data pipelines and backend integration behind them. Hybrid role in San Francisco (2-3 days/week in office) using Python/Django, AWS, Docker, and PostgreSQL.
- Compensation
- $200K–$275K / yr
- Location
- San Francisco
- Work setup
- Hybrid
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 5+ years experience building and deploying applications in production in a backend/data engineering capacity
- Experience developing LLM-based systems for ingesting and evaluating unstructured records and integrating them with user-facing features
- Deep understanding of LLM limitations and best practices for reliable, consistent, accurate outputs
- Customer obsessed and motivated to build a best-in-class model for behavioral health clinical assessments
- Collaborative team player with a focus on delivering measurable results
Nice to have
- Worked with medical records to evaluate whether a patient's history meets criteria for evaluations or assessments
- Experience wearing multiple hats as a generalist backend engineer
- Experience with data pipelines, Python, and related data science/ML libraries
- Significant experience with healthcare data and HIPAA best practices
- Knowledge of modern LLM/ML infrastructure and MLOps best practices
Full description
About Onos Health
Onos Health’s mission is simple but ambitious: ensure every healthcare dollar goes toward delivering the highest quality care. Today, 30% of total U.S. healthcare spending is wasted due to ineffective care and administrative burden caused by misalignment between providers and payers.
Onos is addressing this by building the largest AI-driven healthcare data platform. Our models enables payers to make faster, more accurate decisions across their populations. By guiding members to the right care, Onos is channeling more dollars to high-quality care that drives better outcomes while making healthcare more affordable.
Onos is well-funded by some of the best healthcare investors and is working with the nation’s largest health plans. Come join a category-defining company and help reimagine healthcare for the better.
Why Onos?
- Meaningful impact: Help fix what is fundamentally broken in healthcare
- Direct collaboration: Work alongside experienced founders with deep healthcare and data expertise
- Culture: Join a high-performing, transparent, and results-oriented team
- Ownership: Significant responsibility and autonomy from day one
- Opportunity: Play a pivotal role in building a fast-growing, category-defining healthcare AI company
The Role
We're seeking an experienced and highly capable AI engineer who is motivated to meaningfully improve the way healthcare is administered in the United States. You'll take ownership of developing a significant part of the Onos platform, including the development of models for assessing the level of appropriate care for patients. As an early team member, you'll be expected to wear multiple hats, including acting as a backend/data engineer, while ensuring excellent outcomes for our customers. This role is a hybrid role based in San Francisco, where you'll be expected to work at our office in person 2-3 times a week.
What you'll be doing at Onos:
- Develop LLM/NLU systems to process and extract meaningful information from clinical notes and medical documents, classify patients according to level-of-care guidelines, and make accurate recommendations
- Establish best practices for LLM/AI systems, benchmarking/evaluation frameworks, and model governance to improve reliability, maintainability, and scalability
- Build data pipelines that scale efficiently while maintaining strict data privacy and security standards
- Collaborate with backend engineers to integrate AI/ML capabilities seamlessly into the Onos platform
Technical Challenges At Onos:
- Build and operationalize AI/data pipelines to analyze medical records to streamline clinical assessments and healthcare quality reviews