Senior Software Engineer - Windows AI Agent

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Imagine playing a pivotal role in shaping the future of AI by building the core infrastructure that powers next-generation AI (Artificial Intelligence) models. The Windows AI team is at the forefront of developing cutting-edge AI solutions that enhance user experiences across billions of devices. We are seeking a Senior Software Engineer - Windows AI Agent to focus on scalable model infrastructure, automation, and cloud-based AI workflows for fine-tuning local models, specifically Phi model.

This role is ideal for engineers who thrive on solving complex infrastructure challenges and have a deep understanding of cloud-based AI model deployment, fine-tuning pipelines, and automation. You will play a critical role in ensuring that our AI models operate efficiently, scale seamlessly, and integrate smoothly with local AI environments.

This position is remote eligible within the U.S with preference for ability to be on-site in Redmond, WA.

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 such as AI AND 4+ years technical engineering experience with coding in languages including, but not limited to C, C++, C#, Java, or Python
  • OR equivalent experience.
• 3+ years experience in Python, Scala, or Java for building scalable data workflows.
• 1+ years experience with containerization, orchestration, and cloud-native deployments (such as: Kubernetes, Docker, Ray, MLflow).

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

• PhD in Machine Learning, AI, Computer Science, or a related field (or equivalent industry experience).
• Experience with distributed model training and inference systems (such as TensorFlow, Pytorch, ONNX Runtime, LlamaFactory).
• 2+ years of experience with AI model lifecycle management and continuous deployment practices.

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 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 $153,600 - $250,200 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

Microsoft will accept applications for the role until March 20, 2025.

• Model Infrastructure Development: Design and build scalable, high-performance AI model serving and fine-tuning infrastructure in cloud and edge environments.
• Fine-Tuning and Optimization: Enable efficient fine-tuning workflows for Phi models, optimizing for performance, latency, and cost.
• Scalability & Performance: Identify and resolve bottlenecks in model inference and training pipelines, ensuring efficient execution across diverse hardware platforms.
• Collaboration: Work closely with Machine Learning (ML) researchers, AI engineers, and infrastructure teams to integrate AI models into local environments and cloud ecosystems.
• Innovation & Best Practices: Stay ahead of emerging trends in AI infrastructure, distributed training, and model deployment to continuously enhance the team’s capabilities
• Cloud-Based Model Automation: Develop end-to-end automation pipelines for model training, evaluation, and deployment using cloud services (Azure, AWS, GCP).