New 
Principal Machine Learning Engineer
 Microsoft | |
 United States, Washington, Redmond | |
 Oct 08, 2025 | |
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OverviewOneDrive & SharePoint (ODSP) Applied Science's mission is to invent the AInative knowledge substrate for Microsoft 365-turning the world's largest enterprise content platform (ODSP) into a foundation for specialized, enterpriseready, planetscale AI and agents. We're building durable memory and statefulness for AI, advancing trustworthy solutions for LLMs to interact with data, and optimizing endtoend systems for quality, cost, and latency at massive global scale.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.
ResponsibilitiesDesign 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, robustpipelines,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 companyobjectives,including roadmaps and long-term vision.Conduct applied science experiments,createandvalidatemetrics, develop machine learning pipelines and modeling algorithmsin areas including LLMs, NLP, and Information Retrieval. | |