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Microsoft Member Technical Staff Data Research Engineer Taiwan, Taoyuan City 787526971
Yesterday
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At Microsoft AI, data is at the heart of innovation—and in this role, you will collaborate closely with scientists, engineers, and annotators to curate, analyze, and evaluate diverse multimodal data sources critical to model development. You’ll lead efforts in developing novel data collection strategies, improving dataset quality, understanding data-driven model behaviors, and aligning datasets with ethical and societal values.
Required Qualifications:
- Bachelor's Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or a related technical field AND technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.)
- OR equivalent experience
- Experience in data analysis or data engineering
- Proficiency in statistics and exploratory data analysis methods
- Ability to communicate technical findings effectively to research and product teams
Preferred Qualifications:
- Master's Degree in Computer Science or related technical field AND technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.)
- OR equivalent experience.
- Familiarity with data processing frameworks such as Spark, Ray, Apache Beam
- Experience working with large-scale, real-world datasets that are unstructured or semi-structured
Responsibilities
- 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
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