Principal MLOps / DataOps Engineer, Horizon Surgical Systems

• 5–10+ years of experience in data engineering, MLOps, or DataOps roles
• Strong experience building production-grade data pipelines at scale
• Proficiency in Python and SQL
• Experience with workflow orchestration tools (e.g., Dagster, Airflow, Prefect)
• Hands-on experience with AWS (S3, EC2, Lambda, Batch, IAM, etc.)
• Experience designing data lakes or lakehouse architectures
• Familiarity with dataset versioning and experiment tracking tools
• Experience handling large-scale unstructured data (video, images, sensor data)
• Strong understanding of data quality, validation, and observability

Desirable

• You will own the end-to-end data pipeline for a surgical robotics platform operating in live clinical environments
• This includes ingesting data from field-deployed medical devices, managing scalable data infrastructure, and ensuring high-quality datasets for training and validating machine learning models
• The role spans data engineering and MLOps in an environment with quality/regulatory compliance constraints
• The system must handle continuous inflow of multimodal surgical data (imaging, device telemetry), transform it into structured datasets, and support reliable model development under medical device constraints
• Architect and maintain pipelines from device data capture → cloud ingestion → storage → downstream consumption
• Ensure data integrity, traceability, and auditability across the pipeline
• Build scalable ETL workflows for large multimodal datasets
• Implement dataset lineage and reproducibility standards
• Design workflows to for data curation and label QA
• Integrate pipelines with training workflows and experiment tracking
• Support CI/CD for data and model pipelines
• Own cloud-based data architecture (storage, compute, orchestration)