Overview

The Microsoft AI Super Intelligence Post-Training team is dedicated to advancing post-training methods for LLMs. 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, De Ber Ta, Oscar, Rho-1, Florence, and the open-source Phi models.

We are looking for a Member of Technical Staff with significant experience in large-scale model training, data curation, and hands-on coding. You will help in developing LLMs, SLMs, multimodal, and coding models using both proprietary and open-source frameworks. Key responsibilities include improving model quality and training efficiency through advanced techniques and data strategies, and managing the full pipeline from data ingestion, evaluation, to inference.

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 high-quality code and debug complex systems, document their work clearly, and demonstrate solid experience in shipping ML systems.

Responsibilities:

• Implement large-scale model training, especially with LLMs, SLMs, multimodal, or code-specific models.

• Develop robust evaluation frameworks to assess model performance, conduct systematic benchmarking, and address identified weaknesses while ensuring compliance with customer standards.

• Write efficient, production-quality code and debug complex distributed systems.

• Build and maintain internal tools to streamline training and evaluation workflows and automate repetitive tasks within secure development environments.

Qualifications:

Required Qualifications:

• Bachelor’s or master’s degree in computer science, Engineering, or a related field, or equivalent practical experience.

• 5+ years of professional experience, including 2+ years with Python and ML frameworks such as Py Torch or Tensor Flow.

• 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.

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.