Principal Machine Learning Engineer

We are seeking a passionate, creative, and analytical individual contributor with expertise in training or fine-tuning large language models, including both text and multimodal systems, reinforcement learning, agentic AI architectures, and inference optimization. As a Principal Machine Learning Engineer, you will collaborate with a team of passionate engineers and applied scientists, driving ideas to impactful results in a fast-paced environment. You will work on designing, building, and deploying large-scale machine learning and agentic systems, with an emphasis on production-grade solutions involving data pipelines, large-scale training, model serving, and performance optimization. You are experienced in machine learning engineering from ideation and algorithm selection to architecture and implementation, to deployment and continuous improvement.

Ability to meet Microsoft, customer, and/or government security screening requirements 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.

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 Python, C, C++, C#, or Java
  • OR equivalent experience.
• Proficiency with machine learning frameworks such as PyTorch or scikit-learn.
• 3+ years of experience in coding and design, specifically in the development of AI models and agents for scaled production services.   
  
• 3+ years of experience in shipping applied research to production, highlighting a track record of combining coding skills with advanced expertise in AI model development.

Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements 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: 

• Master's or doctoral degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, Python, C, C++, C#, or Java
  • OR equivalent experience.  
    
• 5+ years of experience in coding and design, specifically in the development of AI models and agents for scaled production services.   
  
• 5+ years of experience in shipping applied research to production, highlighting a track record of combining coding skills with advanced expertise in AI model development.

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $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

Microsoft will accept applications for the role until October 14th, 2025.

• Design and develop advanced artificial intelligence (AI) models and agentic systems for real-world applications.
• Own and drive end-to-end model training, including data pipeline design, distributed training optimization, and performance evaluation.
• Stay up to date with the latest advancements in large language models (LLM), natural language processing (NLP), deep learning, search, and AI research.
• Collaborate closely with applied scientists and other engineering teams to productionize models, build scalable, robust pipelines, and provide support for in-production AI models and agents.
• Contribute to the team's strategic vision, the organization’s scientific direction, and align them with the overall company objectives, including roadmaps and long-term vision. 
• Conduct applied science experiments, create and validate metrics, develop machine learning pipelines and modeling algorithms in areas including LLMs, NLP, and Information Retrieval.