About the role
Develop novel data collection strategies Improve dataset quality and integrity Create high-quality datasets for training and evaluation; run experiments on new datasets (data ablations) to assess their impact and determine the most effective data. Develop and maintain scalable data pipelines for multimodal ingestion, preprocessing, filtering, and annotation. Analyze real-world multimodal datasets to assess quality, diversity, relevance, and identify areas for improvement. Build lightweight tools and workflows for dataset auditing, visualization, and versioning. Collaborate with Safety, Ethics, and Governance teams to ensure datasets meet standards for quality, privacy, and responsible AI practices. Embody our culture and values. Bachelor's Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.) Master's Degree in in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or related technical discipline AND 8+ years technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.) OR Bachelor's Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or related technical discipline AND 12+ years technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.) OR equivalent experience. 2+ years of experience in data analysis or data engineering, including work with large-scale datasets that are unstructured or semi-structured. Proficiency in statistics and exploratory data analysis methods. Familiarity with data processing frameworks such as Spark, Ray, or Apache Beam. Ability to communicate technical findings clearly to research and product teams.
