Staff ML Ops Engineer — Healthcare Platform Lead

Monogram Health, Inc; United States; Hybrid; USD 170,000 - 221,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Employee assistance program

Life insurance

Disability insurance

401k with employer match

Paid time off

Holidays and floating day

Job summary

Monogram Health is seeking a Staff Engineer, Machine Learning Operations to architect and scale ML infrastructure and deployment pipelines for clinical initiatives. You will own the end-to-end lifecycle of models, from development to production, ensuring reliability and security in healthcare environments.

You will mentor teams, drive MLOps standards, and balance innovation with production-grade reliability while handling sensitive healthcare data and regulatory requirements.

Qualifications

• Bachelor's degree in computer science, engineering, or related field; master’s preferred.
• 10+ years in software engineering with 5+ years in ML infrastructure/MLOps or production ML systems, Python development.
• Proven track record building and scaling ML platforms.

Responsibilities

• Architect enterprise-grade ML infrastructure and CI/CD pipelines with monitoring for drift and data quality.
• Mentor data scientists and engineers on production best practices and scalable design.
• Own model lifecycle from development to production with real-time and batch inference and automated retraining.
• Collaborate with clinical and product teams to translate healthcare needs into scalable ML solutions.

Skills

Python development

ML infrastructure

Cloud platforms

Docker & Kubernetes

CI/CD & GitOps

MLOps tooling

Leadership & mentoring

Education

Bachelor's degree in CS/Engineering

Master's degree preferred

Tools

MLflow

Kubeflow

SageMaker

Azure ML

Docker

Kubernetes

Airflow

Terraform

Job description

Monogram Health is seeking a Staff Engineer, Machine Learning Operations to architect and scale ML infrastructure and deployment pipelines for clinical initiatives. You will own the end-to-end lifecycle of models, from development to production, ensuring reliability and security in healthcare environments.

You will mentor teams, drive MLOps standards, and balance innovation with production-grade reliability while handling sensitive healthcare data and regulatory requirements.