General Application — Data & AI/ML Engineering

System; New York (NY); Hybrid; USD 100,000 - 140,000; Full time

Job summary

System is seeking a Data & AI/ML Engineer to design and maintain scalable data pipelines. The ideal candidate will have experience in data engineering and a strong proficiency in Python and SQL, as well as familiarity with cloud platforms like AWS, GCP, or Azure.

This role is focused on building infrastructure for ML models and requires 4+ years of relevant experience. A commitment to purpose-driven work, particularly in technology and healthcare, aligns well with our values at System.

Qualifications

• 4+ years of experience in data engineering, ML engineering, or a related discipline.
• Experience building and maintaining cloud data infrastructure.
• Understanding of ML lifecycle management and model versioning.

Responsibilities

• Design and maintain scalable data pipelines and ETL/ELT workflows.
• Build infrastructure for training and deploying ML models.
• Partner closely with Research and Data Science teams.

Skills

Python

SQL

Spark

dbt

Airflow

Education

Bachelor's or Master's degree in Computer Science or related field

Tools

AWS

GCP

Azure

Docker

Kubernetes

Job description

As a Data & AI/ML Engineer at System, you will:

• 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

The ideal candidate has:

• 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
• Experience building and maintaining cloud data infrastructure (AWS, GCP, or Azure)
• Understanding of ML lifecycle management, model versioning, and deployment patterns
• Comfort with systems design principles applied to data‑intensive architectures

Bonus if you have:

• Experience with containerization and orchestration (Docker, 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

You might be a fit if you:

• Think in systems — mapping feedback loops, interdependencies, and second‑order effects comes naturally to you
• Are motivated by purpose‑driven work, particularly at the intersection of technology and healthcare
• Believe technology should be a force for good and are drawn to the Public Benefit Corporation model
• Hold yourself and your work to a values‑first standard, not just a deliverables‑first one
• Lead with first principles and are comfortable questioning assumptions others take for granted
• See complexity as an invitation, not an obstacle — you thrive when problems are messy and interconnected
• Care about the downstream effects of what you build — on users, on systems, on society
• Operate with intellectual humility — always learning, always open to being wrong

Compensation:

Commensurate with experience and level.

System Inc. is an equal opportunity employer. We are proud to foster a workplace, in person and online, free from discrimination. We strongly believe that diversity of experience, perspectives, and backgrounds will lead to a better environment for our employees and a better product for our users. We are committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Job applicants must be legally authorized to work in the United States of America and must maintain ongoing work authorization during employment.