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Onos Health
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  • Lead Engineer (Machine Learning)  

    - San Francisco
    About Onos HealthOnos Health’s mission is ambitious - ensure every hea... Read More
    About Onos Health

    Onos Health’s mission is ambitious - ensure every healthcare dollar is spent on delivering the highest quality care. With up to 40 cents of every healthcare dollar wasted, Onos’ AI-powered platform is positioned to drive meaningful change in the industry.

    Onos empowers health plans to ensure their members receive the highest quality care while reducing billions in wasteful and abusive spending. By identifying providers who deviate from an evidence-based care standard, we enable health plans to take action before their members' health outcomes are impacted.

    As one of our early engineering hires reporting directly to the CTO, you'll have the opportunity to shape our technical foundation from the ground up. You'll help to build sophisticated AI/ML systems that directly impact mental healthcare delivery while working alongside a team with decades of behavioral health experience.

    Why Onos?

    Meaningful impact: Help improve the quality of care while reducing healthcare costs

    Direct collaboration with experienced founders who have deep healthcare expertise

    Collaborative, transparent & results-oriented culture

    Significant ownership of technical decisions and architecture from day one

    Opportunity to help build and lead the machine learning team

    The Role

    We're seeking a passionate machine learning leader (Senior/Manager level) who is motivated to meaningfully improve the way healthcare is administered in the United States. You'll take ownership of developing our AI/ML systems and models that power the Onos platform through hands-on development and technical leadership. As an early team member, you'll work closely with our founding team while helping to build and lead a growing machine learning team. This role is a hybrid role based in San Francisco, where you'll be expected to work at our office in person at least once a week.

    What you'll be doing at Onos:

    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 best practices, evaluation frameworks, and model governance for future team growth

    Collaborate with backend engineers to integrate ML capabilities seamlessly into the Onos platform

    Technical Challenges At Onos:

    Develop an intelligent engine that ingests complex medical standards of care documents and evaluates provider adherence to guidelines

    Create robust anomaly detection algorithms to identify patterns of fraud, waste, and abuse in healthcare claims

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

    Build ML pipelines to analyze handwritten clinical notes and automate recommendations for clinical reviewers

    Scale our systems to process millions of healthcare records while maintaining strict security and compliance requirements

    What we're looking for:

    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 integrating ML systems with multiple user-facing features

    Familiarity with AWS cloud infrastructure for deploying ML solutions

    Customer obsessed and motivated to make an impact in the healthcare space

    A collaborative team player with a focus on delivering measurable results

    Bonus points if you have:

    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

    Led teams of ML engineers or data scientists on major projects

    Knowledge of modern ML infrastructure and MLOps best practices

    Built and deployed large language models for production applications

    Benefits and Perks

    Flexible hybrid arrangement: 1-2 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

    Regular team events and offsites

    Company-provided equipment and home office setup

    We 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.

    #J-18808-Ljbffr Read Less
  • Lead Engineer (Machine Learning)  

    - San Francisco
    About Onos HealthOnos Health’s mission is ambitious - ensure every hea... Read More
    About Onos Health

    Onos Health’s mission is ambitious - ensure every healthcare dollar is spent on delivering the highest quality care. With up to 40 cents of every healthcare dollar wasted, Onos’ AI-powered platform is positioned to drive meaningful change in the industry.

    Onos empowers health plans to ensure their members receive the highest quality care while reducing billions in wasteful and abusive spending. By identifying providers who deviate from an evidence-based care standard, we enable health plans to take action before their members' health outcomes are impacted.

    As one of our early engineering hires reporting directly to the CTO, you'll have the opportunity to shape our technical foundation from the ground up. You'll help to build sophisticated AI/ML systems that directly impact mental healthcare delivery while working alongside a team with decades of behavioral health experience.

    Why Onos?

    Meaningful impact: Help improve the quality of care while reducing healthcare costs

    Direct collaboration with experienced founders who have deep healthcare expertise

    Collaborative, transparent & results-oriented culture

    Significant ownership of technical decisions and architecture from day one

    Opportunity to help build and lead the machine learning team

    The Role

    We're seeking a passionate machine learning leader (Senior/Manager level) who is motivated to meaningfully improve the way healthcare is administered in the United States. You'll take ownership of developing our AI/ML systems and models that power the Onos platform through hands-on development and technical leadership. As an early team member, you'll work closely with our founding team while helping to build and lead a growing machine learning team. This role is a hybrid role based in San Francisco, where you'll be expected to work at our office in person at least once a week.

    What you'll be doing at Onos:

    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 best practices, evaluation frameworks, and model governance for future team growth

    Collaborate with backend engineers to integrate ML capabilities seamlessly into the Onos platform

    Technical Challenges At Onos:

    Develop an intelligent engine that ingests complex medical standards of care documents and evaluates provider adherence to guidelines

    Create robust anomaly detection algorithms to identify patterns of fraud, waste, and abuse in healthcare claims

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

    Build ML pipelines to analyze handwritten clinical notes and automate recommendations for clinical reviewers

    Scale our systems to process millions of healthcare records while maintaining strict security and compliance requirements

    What we're looking for:

    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 integrating ML systems with multiple user-facing features

    Familiarity with AWS cloud infrastructure for deploying ML solutions

    Customer obsessed and motivated to make an impact in the healthcare space

    A collaborative team player with a focus on delivering measurable results

    Bonus points if you have:

    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

    Led teams of ML engineers or data scientists on major projects

    Knowledge of modern ML infrastructure and MLOps best practices

    Built and deployed large language models for production applications

    Benefits and Perks

    Flexible hybrid arrangement: 1-2 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

    Regular team events and offsites

    Company-provided equipment and home office setup

    We 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.

    #J-18808-Ljbffr Read Less

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