Senior Platform Engineer I, ML Data Systems (24 months fixed-term)

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. 5 years of Software Engineering experience with 3+ of those years working with large ML datasets, especially those in open-source repositories such as Hugging Face. Strong programming skills in Go, Python, SQL, and at least one data pipeline framework (e.g., Airflow, Dagster, Prefect). Experience with data versioning tools (e.g., DVC, LakeFS) and cloud storage systems. Familiarity with machine learning workflows — from training data preparation to evaluation. Familiarity with the architecture and operation of large language models, and a nuanced understanding of their capabilities and limitations. Attention to detail and an obsession with data quality and reproducibility. Motivated by the Khan Academy mission. Proven cross-cultural competency skills.

Evolve and maintain pipelines for transforming raw trace data into ML-ready datasets. Clean, normalize, and enrich data while preserving semantic meaning and consistency. Prepare and format datasets for human labeling, and integrate results into ML datasets. Develop and maintain scalable ETL pipelines using Airflow, DBT, Go, and Python running on GCP. Implement automated tests and validation to detect data drift or labeling inconsistencies. Collaborate with AI engineers, platform developers, and product teams to define data strategies. Contribute to shared tools and documentation for dataset management and AI evaluation. Inform data governance strategies for data retention, PII controls/scrubbing, and sensitive data isolation.