Member of Technical Staff (Data Intelligence)

• Work with model researchers to define what “good data” means for our models, including quality metrics, validation checks, and acceptance thresholds

• Explore open source datasets and create internal ones most suitable to build fundamental World Models

• Build algorithms for automated data quality assessment, data domain mixtures, and domain adaptation from synthetic to real data.

• Track datasets, metadata, provenance, and versions so experiments are reproducible and it’s clear what data went into which training and evaluation runs

• Own CI/CD and development tooling for the data stack (GitHub, Python, PyTorch), and automate repetitive workflows to reduce friction

• Track and optimize throughput, storage, and compute utilization across pipelines and related assets

• Strong ML and deep learning fundamentals with experience building and operating large-scale data and/or compute systems

• Comfortable moving between research questions and production engineering: you can dig into data, run analyses, and also ship reliable systems

• Demonstrated research experience with data compositions, quality, and dataset releases

• Ability to design and execute experiments with convincing unbiased outcomes

• Practical experience with distributed processing and orchestration (Spark, Ray, Airflow, or equivalents)

• Solid Python skills, and familiarity with the tooling around modern model training workflows (datasets, checkpoints, experiment tracking)

• Strong instincts around data quality: how to measure it, how to monitor it, and how to prevent regressions as things scale

• Able to work in a fast-moving environment, prioritize what matters, and communicate clearly with both researchers and engineers

• Bonus: experience with large video datasets, dataset curation for training, or building internal tooling for evaluation/analysis in ML environments