Healthcare Data & AI/ML Systems Engineer

System; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

System, a Public Benefit Corporation, designs pipelines, platforms, and model-serving systems that power healthcare data products with reliability and production-grade quality.

The Data & AI/ML Engineer will design scalable data pipelines, train, deploy, and maintain ML models, and build AI-enabled data infrastructure. You will partner with Research and Data Science to move insights into production, implement observability, and promote cloud-native practices across AWS, GCP, or Azure.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 4+ years of experience in data engineering, ML engineering, or a related discipline.
• Strong proficiency in Python and SQL; experience with Spark, dbt, or Airflow a plus.

Responsibilities

• Design and maintain scalable data pipelines and ETL/ELT workflows
• Build and operate infrastructure for training, deploying, and serving ML models in production
• Develop feature stores, vector databases, and other AI-enabling data infrastructure
• Ensure reliability, low latency, and high availability of data systems
• Partner closely with Research and Data Science to move findings into production
• Implement monitoring and observability for data and model health
• Contribute to infrastructure as code practices and documentation on cloud platforms

Skills

Python

SQL

Data pipelines

System design

MLOps understanding

Education

Bachelor's or Master's in CS/Engineering

Tools

Spark

dbt

Airflow

AWS

GCP

Azure

Job description

System, a Public Benefit Corporation, designs pipelines, platforms, and model-serving systems that power healthcare data products with reliability and production-grade quality.

The Data & AI/ML Engineer will design scalable data pipelines, train, deploy, and maintain ML models, and build AI-enabled data infrastructure. You will partner with Research and Data Science to move insights into production, implement observability, and promote cloud-native practices across AWS, GCP, or Azure.