Staff Machine Learning Operations Engineer

Garner is on a mission to transform the U.S. healthcare system and were 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 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 youve ever wanted your work to solve a problem that touches every person in this country this is the opportunity to do exactly that. Youd 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.

We are seeking an exceptional Staff MLOps Engineer to join our Platform Engineering team. This role will report to the VP of Platform Engineering. As Garners 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.

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.

• Own the reliability performance functionality and cost-efficiency of Garners production ML systems including establishing SLOs observability and on-call responsibilities.
• Architect Garners 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 teams 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

• 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

Youll work on problems that matter at a company working to change healthcare at scale. Youll 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 youll take on real ambitious problems with real ownership and autonomy alongside exceptional principles-based people who genuinely want you to win. Its demanding by design. Youll 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 isnt the right fit for everyone and thats intentional. The people here are driven by whats at stake for real people and thats what gives our intensity its purpose.

• 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.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

The target salary range for this position is $298000 - $351000. Individual compensation for this role will depend on various factors including qualifications skills and applicable 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.