The Microsoft CoreAI Post-Training team is dedicated to advancing post-training methods for both OpenAI and open-source models. Their work encompasses continual pre-training, large-scale deep reinforcement learning running on extensive GPU resources, and significant efforts to curate and synthesize training data. In addition, the team employs various fine-tuning approaches to support both research and product development. The team also develops advanced AI technologies that integrate language and multi-modality for a range of Microsoft products. The team is particularly active in developing code-specific models, including those used in Github Copilot and Visual Studio Code, such as code completion model and the software engineering (SWE) agent models. The team has also produced publications as by-products, including work such as LoRA, DeBerTa, Oscar, Rho-1, Florence, and the open-source Phi models. We are looking for a Principal Machine Learning Engineer - CoreAI with significant experience in large-scale model deployment, production systems, and engineering excellence, ideally from leading technology companies. You will build and optimize production systems for LLMs, SLMs, multimodal, and coding models using both proprietary and open-source frameworks. Key responsibilities include ensuring model reliability, inference performance, and scalability in production environments, and managing the full engineering pipeline from model training, serving, monitoring, to continuous deployment. Our team values startup-style efficiency and practical problem-solving. We are seeking a curious, adaptable problem-solver who thrives on continuous learning, embraces changing priorities, and is motivated by creating meaningful impact. Candidates must be self-driven, able to write production-grade code and debug complex distributed systems, document engineering decisions, and demonstrate a track record in shipping ML systems at scale. The ability to quickly translate ideas into working code for rapid experimentation would be a plus.