Principal Software Engineer, The Windows AI Agent team

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Principal Software Engineer, The Windows AI Agent team

Imagine playing a pivotal role in shaping the future of AI (Artificial Intelligence) by building the foundational data infrastructure to train next-generation models. The Windows AI Agent team is at the forefront of developing cutting-edge AI solutions that enhance user experiences across billions of devices. We are seeking a Principal Software Engineer to focus on building scalable data pipelines, automating data distillation, and optimizing data workflows for fine-tuning local AI SOTA models (<7B).

This role is ideal for engineers who thrive on solving complex data challenges and have a deep understanding of cloud-based data engineering, model fine-tuning workflows, and automation. As a Principal Software Engineer, you will play a critical role in ensuring that our AI models are trained on high-quality, efficiently processed data while enabling seamless automation for continuous improvements.

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

Required Qualifications

• Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience
• 3+ years of experience in software engineering, with a direct focus on data pipelines, automation, and cloud computing
• Demonstrated experience and proficiency in Python, Scala, or Java for building scalable data workflows.
• Demonstrated understanding of data quality, feature engineering, and distributed computing in Machine Learning (ML) workflows.

Other Requirements

Candidates must be able to meet Microsoft, customer and/or government security screening requirements that are required for this role. These requirements include, but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Preferred Qualifications

• Bachelor's Degree in Computer Science or related technical field AND 10+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
• PhD in Machine Learning, AI, Computer Science, or a related field (or equivalent industry experience).
• Prior experience supporting model training or fine-tuning workflows in production is a plus.

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $137,600 - $267,000 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 $180,400 - $294,000 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

Microsoft will accept applications for the role until March 22, 2025.

#W+DJobs

• Data Pipeline Development: Design, build, and maintain scalable, reliable, and efficient data pipelines for collecting, processing, and transforming large-scale datasets.

• Data Distillation & Curation: Implement automated data distillation techniques to refine and extract high-quality training data for local model fine-tuning.

• Cloud Automation: Develop cloud-based workflows and automation for data ingestion, preprocessing, and model training, leveraging services like Azure, AWS, or GCP.

• Model Fine-Tuning Support: Enable seamless data delivery pipelines for Phi model fine-tuning, ensuring efficient use of computational resources.

• Collaboration: Work closely with AI researchers, ML engineers, and infrastructure teams to streamline data workflows and improve AI model performance.

• Innovation & Best Practices: Stay up-to-date with emerging trends in AI data engineering, automation, and cloud technologies to continuously improve the team’s capabilities.