Job description

What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Staff Machine Learning Operations Engineer to join our Platform Engineering team . This role will report to the VP of Platform Engineering . As Garner's foundational dedicated MLOps Engineer, you will assume responsibility for the reliability, performance, and cost-efficiency of our production machine learning systems. You will lead the development of a robust platform designed to facilitate the secure and consistent deployment of models by our machine learning and data science teams. Given that these models directly influence health outcomes and cost-effectiveness for millions of patients, maintaining the highest standards of production quality is imperative. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Own the reliability, performance, functionality, and cost-efficiency of Garner's production ML systems, including establishing SLOs, observability, and on-call responsibilities. Architect Garner's ML platform including required data infrastructure (including feature store, model registry and CI/CD for models), and standardized service patterns. Implement ML-specific CI/CD pipelines: Transition our deployment process from manual notebook hand-offs to automated, PR-driven CI/CD workflows that include automated data quality checks and statistical model validation prior to deployment. Drive down cost and latency through improved architecture, hardware choices, and model optimization as appropriate. Lay the foundation for a future Garner MLOps team, including workflows, standards, and KPIs that enables rapid teammate onboarding and helps stakeholders and teammates quickly identify the health of the team’s products, allowing engineers to focus on areas where issues reside Establish Drift Monitoring: Design and implement automated data drift and concept drift monitoring systems that alert the team when models degrade, laying the groundwork for future Continuous Training (CT) architectures The ideal candidate has: 7+ years of software engineering experience, with significant time spent operating ML or data-intensive systems in production at scale. Deep experience with the modern ML production stack: model serving (e.g., Sagemaker, Triton, or equivalent), feature stores, model registries, and CI/CD for ML. Strong infrastructure and platform engineering fundamentals: Kubernetes, containerization, cloud (AWS preferred), Terraform/IaC, observability, and incident response. Experience designing ML platforms or significant components of one (not strictly consuming SaaS) and the judgment to know when to build vs. buy. Strong collaboration with ML, data, platform engineers, data scientists, and product engineering teams, with the ability to set technical direction as the most senior MLOps voice in the org. Healthcare, regulated-data, or other high-stakes production ML experience is a plus but not required. A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you’ll get here: You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use: Python, Kubernetes, AWS, Sagemaker, Terraform, S3, Snowflake, Airflow, Datadog This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare. Compensation Transparency: The target salary range for this position is $298,000 - $351,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Requirements

Must-haves

What you’ll be part of
Own the reliability, performance, functionality, and cost-efficiency of Garner's production ML systems, including establishing SLOs, observability, and on-call responsibilities.
Architect Garner's ML platform including required data infrastructure (including feature store, model registry and CI/CD for models), and standardized service patterns.
Drive down cost and latency through improved architecture, hardware choices, and model optimization as appropriate.
Establish Drift Monitoring: Design and implement automated data drift and concept drift monitoring systems that alert the team when models degrade, laying the groundwork for future Continuous Training (CT) architectures
7+ years of software engineering experience, with significant time spent operating ML or data-intensive systems in production at scale.
Deep experience with the modern ML production stack: model serving (e.g., Sagemaker, Triton, or equivalent), feature stores, model registries, and CI/CD for ML.
Experience designing ML platforms or significant components of one (not strictly consuming SaaS) and the judgment to know when to build vs. buy.
Strong collaboration with ML, data, platform engineers, data scientists, and product engineering teams, with the ability to set technical direction as the most senior MLOps voice in the org.
A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback
Python, Kubernetes, AWS, Sagemaker, Terraform, S3, Snowflake, Airflow, Datadog
This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Nice-to-haves

Strong infrastructure and platform engineering fundamentals: Kubernetes, containerization, cloud (AWS preferred), Terraform/IaC, observability, and incident response.
Healthcare, regulated-data, or other high-stakes production ML experience is a plus but not required.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlci/cdkubernetesawspython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto