Design and improve data pipelines that process large, multi-modal datasets from a variety of internal and external sources into training datasets for AI models.
Evolve our data storage layer to support analytics, schema evolution, reproducibility, and efficient data access.
Collaborate with ML engineers to improve the performance and reliability of Python-based data processing workflows.

Requirements

Minimum of 8 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or a PhD with 3 years experience; or equivalent experience.
Proven ability to design flexible, maintainable ETL systems.
Experience with data pipeline orchestration tools such as Prefect, Airflow, Argo, Databricks, or Spark.
Understanding of the ML model lifecycle; prior work with scientific or ML workflows is a plus.
Hands-on experience with multi-terabyte scale data processing.
Familiarity with AWS; Kubernetes experience is a bonus.
Knowledge of data lake technologies such as Parquet, Iceberg, AWS Glue etc.
Strong Python software engineering skills.
Pragmatic mindset — able to evaluate tradeoffs find solutions that empower ML researchers to move quickly.
Background in bioinformatics or chemistry is a plus.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

ETL systemsdata pipeline orchestrationPythondata processingdata lake technologiesAWSKubernetesParquetIcebergAWS Glue

collaborationproblem-solvingpragmatic mindset