Senior Machine Learning Engineer - Machine Learning Infrastructure
FlipFull Time
Senior (5 to 8 years)
The candidate must have 8+ years of experience in Data Pipeline Development, specifically building and maintaining scalable ETL/ELT pipelines for ML/AI training workflows using tools like AWS Glue, DBT, Dagster, Spark, or Ray. Strong proficiency in Spark, Python, and SQL for feature engineering, data transformation, and ensuring high-quality, versioned datasets is required. Additionally, 8+ years of Cloud Infrastructure & Data Warehousing experience, with at least 4 years focused on AWS services like Redshift, S3, Glue, IAM, EMR, and SageMaker, is necessary. Experience optimizing data warehouses and managing data lakes for large-scale, versioned ML training datasets, including partitioning and access controls, is essential. Familiarity with data governance practices, such as metadata management and lineage tracking, is also required.
The Staff Data Engineer will build and maintain data pipelines to extract information from central storage systems for training machine learning models and AI tools. Responsibilities include designing reliable data flows, testing for accuracy, solving unexpected challenges, and transforming raw information into useful resources. This role supports innovations that improve patient outcomes and provider experiences by ensuring high-fidelity training data through scalable data validation, quality checks, and error-handling mechanisms, including bias detection and anomaly identification.
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