5+ years of experience building and deploying machine learning systems in production
Strong experience designing data pipelines in Python and relevant data science/ML libraries
Familiarity with developing LLM/NLU systems and integrating them with user-facing features
In-depth understanding of the limitations and best practices for LLMs
Passionate about making a significant impact in healthcare
Team-oriented with a focus on delivering measurable results
Responsibilities
Design and deploy advanced ML models to analyze healthcare data and provide insights
Develop systems for processing clinical notes and medical documents using LLMs
Construct scalable data pipelines while ensuring adherence to privacy standards
Set ML & AI best practices and governance frameworks for team scalability
Collaborate with backend engineers for seamless integration of AI/ML solutions
Benefits
Flexible hybrid work model with remote-first culture
Unlimited vacation policy for work-life balance
Comprehensive medical, dental, and vision insurance
Significant equity as an early employee
Paid parental leave promoting family support
401(k) retirement plan
Regular team events fostering company culture
Direct mentorship from experienced healthcare founders
Full Job Description
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
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
Development Tools: Github, Jira, CoderabbitAI, Tusk, Claude
What we're looking for:
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
Bonus points if you have:
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 wearing multiple hats as a generalist backend engineer
Experience working with data pipelines and Python and related data science/ML libraries
Significant experience working with healthcare data and with HIPAA best practices
Knowledge of modern LLM and ML infrastructure and MLOps best practices
Benefits and Perks
Flexible hybrid arrangement: 2-3 days/week at San Francisco office (Financial District), remote-first culture
Unlimited vacation policy
Paid parental leave
Medical, dental, and vision insurance
Pre-tax commuter benefits
401(k)
Significant equity as an early employee
Direct mentorship from experienced founders
Ground-floor opportunity to help build a team and culture
