Garner Health

1 month ago

Staff Machine Learning Operations Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Staff MLOps Engineer to own the reliability, performance, and cost-efficiency of Garner Health’s production ML systems. You’ll architect and implement the ML platform (feature store, model registry, CI/CD for models), drive cost/latency improvements, establish drift monitoring, and lay foundations for a future MLOps team within a healthcare-focused platform engineering function.

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•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

Pay and Benefits

Salary: USD 298,000 - 351,000 annually

Equity and Bonus:Equity

Perks:Paid Leave401kHealth Insurance

Key Requirements

•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

Experience:7+ yearsHealthcareData-intensiveMlopsPlatform engineering

Skills:CollaborationCommunicationProblem-solving

Languages:English

Tech Stack:PythonKubernetesAWSSagemakerTerraformS3SnowflakeAirflowDatadog

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Garner Health

Provides employer-sponsored healthcare navigation and care coordination services, combining personalized digital coaching, benefits navigation, and care management to help employees access appropriate care and reduce healthcare costs.

Industry: HealthTech

Website

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Garner Health

1 month ago

Staff Machine Learning Operations Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•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

Pay and Benefits

Salary: USD 298,000 - 351,000 annually

Equity and Bonus:Equity

Perks:Paid Leave401kHealth Insurance

Key Requirements

•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

Experience:7+ yearsHealthcareData-intensiveMlopsPlatform engineering

Skills:CollaborationCommunicationProblem-solving

Languages:English

Tech Stack:PythonKubernetesAWSSagemakerTerraformS3SnowflakeAirflowDatadog

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Garner Health

Industry: HealthTech

Website