Staff MLOps Engineer, Healthcare AI

Take a Seat Tech; New York (NY); Hybrid; USD 298,000 - 351,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

Take a Seat Tech in New York City (Hybrid) is seeking a Staff MLOps Engineer to build and own the machine learning production platform. You will deploy models, monitor systems, and run scalable infrastructure for healthcare AI applications.

The role emphasizes production reliability, cost optimization, and close collaboration with data science and engineering teams. Equity and benefits are included as part of the compensation package.

Qualifications

• Seven or more years of software engineering, including ML or data-intensive systems.
• Strong infrastructure fundamentals and experience designing platform components.
• Experience building deployment and observability tooling for ML models.

Responsibilities

• Build model deployment pipelines, feature store and registry capabilities.
• Build production monitoring and automated validation.
• Detect data and model drift.
• Improve inference cost, latency and reliability.

Skills

ML systems

Software engineering

Infrastructure fundamentals

Platform design

Data-intensive systems

Python

Model monitoring

Tools

Kubernetes

AWS

SageMaker

Terraform

Job description

# Staff MLOps Engineer, Healthcare AIA hybrid New York staff MLOps role building the machine learning production platform at a healthcare technology company, paying $298,000 to $351,000 plus equity and benefits.$298,000 to $351,000 a year New York, New York (Hybrid)FULL\\_TIMEBrowse open rolesHealthcare AIData & AIFULL\\_TIME## About the roleTake models from promising to dependable.A healthcare technology company is hiring a Staff MLOps Engineer to lay the foundations of its machine learning production platform. You will own how models are deployed, monitored and run, not just how they perform in development.At a glanceSalary: $298,000 to $351,000 a year, plus equity and benefitsLocation: New York City. Hybrid, in the office Tuesday to ThursdayStack: Python, Kubernetes, AWS, SageMaker, Terraform, model monitoringWhat you will doBuild model deployment pipelines, feature store and registry capabilities.Build production monitoring and automated validation.Detect data and model drift.Improve inference cost, latency and reliability.What you will bringSeven or more years of software engineering, including substantial work running ML or data-intensive systems.Strong infrastructure fundamentals and experience designing platform components.Why this roleThe chance to shape the MLOps function and its standards, as a technical partner to data science and engineering.Good to knowThe role includes production reliability and on-call duties. Current or future visa sponsorship is not available.How to applyApply with your resume and a summary of the ML systems you have operated.