Staff Software Engineer (AI)

About Onos HealthOnos 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 enable 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 outcomes while making healthcare more affordable. Onos recently closed a $6M Seed round with top-tier investors and is already working with some of the nation's largest health plans, signing its first national plan just months after launch. Come join a category-defining company and help reimagine healthcare for the better. Why Onos?Meaningful impact: Help fix what is fundamentally broken in healthcareDirect collaboration: Work alongside experienced founders with deep healthcare and data expertiseCulture: Join a high-performing, transparent, and results-oriented teamOwnership: Significant responsibility and autonomy from day oneOpportunity: Play a pivotal role in building a fast-growing, category-defining healthcare AI companyThe RoleWe'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 OnosDevelop 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 recommendationsEstablish best practices for LLM/AI systems, benchmarking/evaluation frameworks, and model governance to improve reliability, maintainability, and scalabilityBuild data pipelines that scale efficiently while maintaining strict data privacy and security standardsCollaborate with backend engineers to integrate AI/ML capabilities seamlessly into the Onos platformTechnical Challenges At OnosBuild and operationalize AI/data pipelines to analyze medical records to streamline clinical assessments and healthcare quality reviewsBenchmark LLM systems to more accurately and reliably extract evidence from medical records and classify patients' level-of-care recommendationsDevelop and optimize a system that ingests complex medical standards of care documents and evaluates provider adherence to guidelinesDesign explainable AI solutions that provide transparency into model decisions for healthcare professionalsTech Stack: Infrastructure/Systems: AWS (ECS, Bedrock, Cognito, etc.), Docker, Github ActionsLanguages/Frameworks: Python, Django, Celery, django-ninja, django-tenantsDatabase/Storage: PostgreSQL (AWS RDS), S3Development Tools: Github, Jira, CoderabbitAI, Tusk, ClaudeWhat We're Looking For5+ years experience building and deploying applications in production in a backend engineering / data engineering capacityRelevant experience with developing LLM-based systems for ingesting and evaluating unstructured records for industry-specific use cases and integrating them with user-facing featuresDeep understanding of the limitations of using LLMs and the best practices for using them for reliable, consistent, and accurate outputsCustomer obsessed and motivated to build a best-in-class model for behavioral health clinical assessments in the healthcare spaceA collaborative team player with a focus on delivering measurable resultsBonus Points If You HaveSpecifically 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 wearing multiple hats as a generalist backend engineerExperience working with data pipelines and Python and related data science/ML librariesSignificant experience working with healthcare data and with HIPAA best practicesKnowledge of modern LLM and ML infrastructure and MLOps best practicesBenefits And PerksFlexible hybrid arrangement: 2-3 days/week at San Francisco office (Financial District), remote-first cultureUnlimited vacation policyPaid parental leaveMedical, dental, and vision insurancePre-tax commuter benefits401(k)Significant equity as an early employeeDirect mentorship from experienced foundersGround-floor opportunity to help build a team and cultureRegular team events and offsitesCompany-provided equipment and home office setupWe are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.