General Application — Data & AI/ML Engineering

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