Senior Platform Engineer, AI Evaluation

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 AI Platform Engineer to evolve and extend our internal evaluation framework for assessing the quality of our AI‑driven experiences at Khan Academy. This engineer will have worked with enough eval systems to quickly make sense of Khan's internal eval framework and recognize opportunities for improvement. This is largely a software development role, but domain experience with AI eval is essential for appreciating the hill‑climbing and data science workflows we need to support.

Soft skills will be important for gathering internal requirements, getting buy‑in for changes, and then developing documentation and training materials. You’ll work closely with ML data engineers and platform developers to help internal teams adopt an eval‑driven development process incorporating offline benchmark tests and online experiments.

As a Platform Engineer focused on evaluation
, you’ll be expected to:

• Be fluent in the range of offline and online evaluation strategies, and when to apply the techniques over the lifecycle of development
• Have intuitions about how to specify eval pipelines succinctly using declarative syntax
• Understand the role of stratified datasets and ground truth labeling
• Appreciate the range of eval scoring schemes from human raters to automated LLMs‑as‑judge
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
• 5 years of Software Engineering experience with 2+ of those years working on the evaluation of generative AI systems
• Strong programming skills in Go, Python, SQL, and at least one data pipeline framework (e.g., Airflow, Dagster, Prefect)
• Familiarity with the architecture of large language models and their industry‑standard APIs

• Experience with labeling platforms (e.g., Label Studio, Scale AI, Toloka) and human‑in‑the‑loop concerns such as rubric development and inter‑rater agreement
• Exposure to MLOps practices such as model registry, feature store, or continuous evaluation
• Background in education technology or other human‑centered AI applications

• Ample paid time off as needed – Your well‑being is a priority
• 8 pre‑scheduled Wellness Days in 2026 occurring on a Monday or a Friday for a 3‑day weekend boost
• Remote‑first culture - that caters to your time zone, with open flexibility as needed, at times
• Generous parental leave
• An exceptional team that trusts you and gives you the freedom to do your best
• The chance to put your talents towards a deeply meaningful mission and the opportunity to work on high‑impact products that are already defining the future of education
• Opportunities to connect through affinity, ally, and social groups
• and we offer all those other typical benefits as well: 401(k) + 4% matching & comprehensive insurance, including medical, dental, vision, and life

At Khan Academy we are committed to fair and equitable compensation practices, the well‑being of our employees, and our Khan community. This…