Software Engineer 2 - Machine Learning Engineer

The Microsoft CoreAI Post-Training team is dedicated to advancing post-training techniques for both OpenAI and open-source models. Our work spans continual pre-training, fine-tuning, reinforcement learning at scale, and data curation. We contribute to both research and product development across Microsoft.
We build and deploy cutting-edge AI technologies that integrate language and multi-modality into Microsoft products. Our team is especially active in developing code-focused models, such as those powering GitHub Copilot and Visual Studio Code. We are looking for a Machine Learning Engineer with a strong foundation in ML systems and a passion for deploying large-scale models in production. This role is ideal for someone who has experience working with LLMs or multimodal models and is eager to grow their expertise in building scalable, reliable ML infrastructure.

Responsibilities

Responsibilities

• Collaborate with senior engineers and researchers to build and optimize training and inference pipelines for LLMs, SLMs, multimodal, and code-specific models.
• Contribute to the deployment and monitoring of models in production environments.
• Write clean, efficient, and maintainable code for ML systems.
• Help improve inference performance, reliability, and scalability.
• Participate in rapid experimentation cycles and support integration with Microsoft products.

Qualifications

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 2+ years of experience with Python and ML frameworks such as PyTorch or TensorFlow.
• Hands-on experience with training or fine-tuning LLMs or multimodal models.
• Familiarity with production ML systems and concepts like model serving, caching, batching, and monitoring.
• Understanding of distributed systems and cloud-based infrastructure.

Preferred Qualifications

• Experience with containerization tools (e.g., Docker, Kubernetes).
• Exposure to MLOps or DevOps practices (CI/CD, automated testing, deployment).
• Interest in generative AI and open-source model ecosystems.
• Ability to work in a fast-paced, collaborative environment with a growth mindset.
• Strong communication and documentation skills.