Software Engineer; AI

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.

• 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

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.

• 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

• Build and operationalize AI/data pipelines to analyze medical records to streamline clinical assessments and healthcare quality reviews

• Benchmark LLM systems to more accurately and reliably extract evidence from medical records and classify patients’ level‑of‑care recommendations

• Develop and optimize a system that ingests complex medical standards of care documents and evaluates provider adherence to guidelines

• Design explainable AI solutions that provide transparency into model decisions for healthcare professionals

Tech Stack:

• Infrastructure/Systems: AWS (ECS, Bedrock, Cognito, etc.), Docker, Github Actions

• Languages/Frameworks:
  Python, Django, Celery, django‑ninja, django‑tenants

• Database/Storage:
  Postgre
  
  SQL (AWS RDS), S3

• Development Tools:
  Github, Jira, Coderabbit
  
  AI, Tusk, Claude

• 5+ years experience building and deploying applications in production in a backend engineering / data engineering capacity

• Relevant experience with developing LLM‑based systems for ingesting and evaluating unstructured records for industry‑specific use cases 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

• Customer obsessed and motivated to build a best‑in‑class model for behavioral health clinical assessments in the healthcare space

• A collaborative team player with a focus on delivering measurable results

• Specifically worked with medical records to evaluate whether a patient’s history meets criteria for evaluations or assessments (e.g., claims authorization or other types of evaluations)

• Experience…