System
Healthcare
GeneralApplication—Data&AI/MLEngineering
Neural analysis suggests this role is
optimal for Mid+ candidates.
“General Application — Data & AI/ML Engineering at System. Skills: Data Engineering, MLOps, Systems Design. Design pipelines. Build pipelines”
Industry & Context.
Systems thinking; First principles thinking; Complex problem solving
Legally authorized to work, Maintain ongoing work authorization
What They're Looking For.
Must Have
5+ years experience with Spark, 5+ years experience with dbt, 5+ years experience with Airflow, Experience building cloud data infrastructure, Experience maintaining cloud data infrastructure, Understanding of ML lifecycle management, Understanding of model versioning, Understanding of deployment patterns, Comfort with systems design principles
Nice to Have
Experience with containerization, Experience with orchestration, Familiarity with knowledge graphs, Familiarity with graph databases, Familiarity with semantic data models, Experience with data migrations, Experience maintaining service availability, Background working with clinical data, Background working with healthcare data, Exposure to LLMOps, Exposure to AI governance frameworks
What You'll Do.
Design model-serving systems
Build model-serving systems
Maintain model-serving systems
How You'll Work.
Team & Collaboration
Interdisciplinary teams; Diverse teams
Full Job Description
Welcome systems thinkers. System builds software to help the world see and solve anything as a system, starting in healthcare. We are a Public Benefit Corporation driven by purpose and shaped by values. We hire systems thinkers who are motivated by our purpose, share our values, and have the skills to advance our mission. At System, this means designing the pipelines, platforms, and model-serving systems that power our healthcare data products — reliably, responsibly, and at production grade. As a Data 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. About System W
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