Senior Platform Engineer, ML Data Systems; Mountain View, CA​/Remote

Mountain View, CA / Remote (Continental US + Hawaii + Canada Only)

Khan Academy is a nonprofit with the mission to deliver a free, world‑class education to anyone, anywhere. Our proven learning platform offers free, high‑quality supplemental learning content and practice that cover Pre‑K - 12th grade and early college core academic subjects, focusing on math and science. We have over 181 million registered learners globally and are committed to improving learning outcomes for students worldwide, focusing on learners in historically under‑resourced communities.

Our students, teachers, and parents come from all walks of life, and so do we. Our team includes people from academia, traditional/non‑traditional education, big tech companies, and tiny startups. We hire great people from diverse backgrounds and experiences because it makes our company stronger. We value diversity, equity, inclusion, and belonging as necessary to achieve our mission and impact the communities we serve.

We know that transforming education starts in‑house with learning about ourselves and our colleagues. We strive to be world‑class in investing in our people and commit to developing you as a professional.

We’re looking for an ML Data Engineer to evolve our eval dataset tools to meet the growing platform needs of AI‑based tutoring at Khan Academy. We’re looking for someone who can gather internal requirements, design schema based on well‑known dataset patterns, and deploy, document, and train people on an internal dataset management framework. The systems you design will need to integrate with trace management and human labeling APIs.

You’ll work closely with other AI engineers, platform developers, and labeling teams to ensure our data is clean, representative, and ready for both human and automated evaluation.

This role bridges ML operations, data engineering and data science— enabling our AI systems to learn from reliable, well‑structured datasets that reflect the diversity and nuance of real learners.

• 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 in support of continuously improving the quality of Khan’s AI‑based tutoring.
• Contribute to shared tools and documentation for dataset management and AI evaluation.
• Inform our data governance strategies for proper data retention, PII controls/scrubbing, and isolation of particularly sensitive data such as offensive test imagery.

• 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 “to provide a free world‑class education for anyone, anywhere.”
• Proven cross‑cultural competency skills demonstrating self‑awareness, awareness of other, and the ability to adopt inclusive perspectives, attitudes, and behaviors to drive inclusion and belonging throughout the organization.

• Experience with labeling platforms (e.g., Label Studio, Scale AI, Toloka) or human‑in‑the‑loop systems.
• Understanding of ML…