Principal Software Engineer, The Windows AI Agent team
 Microsoft | |
 United States, Washington, Redmond | |
 Mar 16, 2025 | |
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OverviewPrincipal Software Engineer, The Windows AI Agent teamImagine 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
ResponsibilitiesData 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. | |