Senior
Data Infrastructure
Overview
Help build the world’s most advanced multimodal dataset at Microsoft AI
We are on a mission to create the largest and most advanced multimodal dataset in the world. This dataset, spanning all modalities from across the web and beyond, will power the training of the world’s most capable AI frontier models, pushing the boundaries of scale, performance, and product deployment.
The AI Data Infra team at Microsoft AI is responsible for building data infrastructure to help MAI teams to generate the biggest and best training dataset. Our work involves data pipelines, Spark, Ray, Vector Databases, and all other aspects of data infra.
We are looking for outstanding individuals excited about contributing to the next generation of systems that will transform the field. In particular, we are looking for candidates who:
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Are passionate about the role of data in large-scale AI model training
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Will thrive in a highly collaborative, fast-paced environment
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Have a high degree of expertise and pay close attention to details
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Demonstrate a proactive attitude and enthusiasm for exploring new methods and technologies
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Effectively manage multiple responsibilities and can adjust to shifting priorities.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction.
This role is part of Microsoft AI's Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being.
We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models!
Responsibilities
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Design and develop data pipelines that ingest enormous amounts of multi-modal training data (text, audio, images, video).
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Own and maintain critical data infrastructures, including spark, ray, vector databases, and others.
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Build and maintain cutting-edge infrastructure that can store and process the petabytes of data needed to power models.
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Partner with the pretraining and post-training teams to improve our data recipe by rigorous and careful experimentation.
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Embody our culture and values.
Qualifications
Required Qualifications:
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Bachelor’s Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling or data engineering work
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OR Master’s Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ year(s) experience in business analytics, data science, software development, or data engineering work
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OR equivalent experience.
Preferred Qualifications:
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Bachelor’s Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 8+ years experience in business analytics, data science, software development, data modeling or data engineering work
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OR Master’s Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years of business analytics, data science, software development, data modeling or data engineering work experience
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OR equivalent experience.
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 IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $160,200 - $261,000 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.
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