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System

Causal, data-driven maps of complex systems

Data & AI/ML Engineering

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science, Engineering, or a related field is required.
  • At least 4 years of experience in data engineering, ML engineering, or a related discipline is required.
  • Strong proficiency in Python and SQL is required.
  • Experience building and maintaining cloud data infrastructure on AWS, GCP, or Azure is required.
  • Understanding of machine learning lifecycle management, model versioning, and deployment patterns is required.
  • Comfort with systems design principles applied to data-intensive architectures is required.

Responsibilities

  • Design and maintain scalable data pipelines and ETL/ELT workflows.
  • Build and operate infrastructure for training, deploying, and serving machine learning 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 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.

Desired Qualifications

  • Experience with Spark, dbt, or Airflow is a plus.
  • Experience with containerization and orchestration using Docker or Kubernetes.
  • Familiarity with knowledge graphs, graph databases, or semantic data models.
  • Experience with data migrations while maintaining service availability.
  • Background working with clinical or healthcare data.
  • Exposure to LLMOps or AI governance frameworks.

About the Company

System

Causal, data-driven maps of complex systems

System, Inc. helps people understand and address complex global problems by turning vast data into system maps. It builds data-driven graphical models that show how factors interrelate—starting with public health topics like COVID-19 and its links to diabetes and obesity—and aggregates thousands of sources, including peer-reviewed research, to illustrate cause-and-effect relationships. The platform is designed for researchers, policymakers, and the public to explore problems, identify intervention points, and make smarter decisions based on how events and conditions influence each other. What sets System apart is its focus on systems thinking and causal inference within a public benefit framework, plus a dedicated platform for visualizing knowledge graphs that connect data to real-world decisions. The company aims to improve collective understanding of complex systems and empower better actions by revealing interdependencies and intervention points across global issues.

Company size
11-50
Company stage
Grant
Total funding
$19M
Headquarters
New York City, New York
Founded
2017

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