Build AI Data Infrastructure and Data Engines
Develop and evolve large-scale AI data infrastructure for frontier AI lab. Own data collection, ingestion, cleaning, curation, generation, governance, metadata management, query , analytic and hybrid search , building reusable, observable, and explainable EB-scale data platform that support high-quality data for pre-training and post-training workloads.

Develop Intelligent Multimodal Data Processing Systems
Lead automated understanding and processing of text, images, video, documents. Build labeling and taxonomy systems, semantic feature extraction, data quality modeling, and automated data governance capabilities. Design and train models for classification, recognition, captioning, quality scoring, prediction, and related data-processing tasks.

Build AI-Native Data Pipelines
Leverage LLMs, VLMs, and Agents to build intelligent data pipelines that automate data collection, filtering, deduplication, quality diagnosis, annotation/re-labeling, generation, scheduling, orchestration, and anomaly detection. Use AI-native workflows to significantly reduce manual data operations and improve pipeline scalability and efficiency.

Build AI-Native Data Storage and Table Layers
Design open, AI-optimized storage using Lance, Iceberg, Paimon, and Parquet. Support multimodal
data and embeddings, fast random access and scans, schema evolution, transactions, versioning/
time travel, indexing, and interoperable access across training and query engines.

Discover and Build Rare, High-Value Datasets
Develop differentiated datasets for challenging domains, including web/PDF/encyclopedic/private/query-based text data as well as real-world multimodal scenarios such as retail inspection, warehouse inspection, healthcare, education, OCR, GUI interaction, and multimodal trajectories. Apply a combination of real-world data collection, human annotation, and synthetic data generation to produce rare and high-value datasets.

Drive the Data–Model–Evaluation Iteration Loop
Use evaluation feedback and model failure analysis to identify data gaps and design targeted datasets and synthetic-data strategies. Establish observable metrics that quantify the contribution of data to model capability improvements, continuously update datasets during training, and build an iterative Data → Model → Evaluation → Data optimization loop.

Partner Closely with Model and Training Teams
Collaborate with model researchers and training engineers on training-data construction, data feedback loops, targeted data mining, and evaluation-driven iteration. Use data as a primary lever for improving model capability and become a core driver of AI model training performance.

• Required:
  Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 3+ years experience in business analytics, data science, software development, data modeling, or data engineering OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience.
  Experience with distributed data Platforms such as Spark, Flink or Ray.
  Proficient experience in Python and experience with SQL and Shell.
  Experience with Multimodal Data (Text, Image, Video, or Audio)
  
  Preferred:
• Hands-on experience building datasets for LLM/VLM/multimodal model pre-training or post-training.
• Experience with synthetic data, including text generation, image-text synthesis, rendering/diffusion-based generation, or multimodal trajectory generation for GUI, search, file-operation, or agentic tasks.
• Familiarity with major evaluation benchmarks such as MMLU, MMBench, MM-BrowseComp, with practical experience using evaluation results and failure analysis to drive targeted data improvements.
• Experience with open file and table formats such as Lance, Iceberg, Paimon and Parquet,including schema evolution, versioning, transactions, indexing, and performance optimization for multimodal AI workloads.
• Solid understanding of LLMs, speech/audio models, vision models, and multimodal models. Hands-on experience with large-scale AI data construction, cleaning, synthesis, or quality evaluation.
• Experience building ETL systems, data models, data pipelines, or data warehouses is strongly preferred. Experience processing large-scale text, image, or video datasets is a plus.
• Familiarity with Agents and modern LLM toolchains, with practical experience—or strong interest—in applying LLMs to data production, analysis, quality control, governance, and pipeline automation.

Data Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

Data Engineering IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.