Microsoft Software Engineer – Machine Learning 

Taiwan, Taoyuan City 

677759060

, Oscar, Rho-1, Florence, and the open-source Phi models.Software Engineer- Machinesignificant experiencein large-scale model training, data curation, and hands-on coding. You will helpdeveloping 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.driven, able to write high-quality code and debug complex systems, document their work clearly, andsolid experience in shipping ML systems.

Required Qualifications

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

• + years of professional experience, including 2+ years with Python and ML frameworks such asPyTorchor 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 toMLOpsor DevOps practices (CI/CD, automated testing, deployment).

• in generative AI and open-source model ecosystems.

• Ability to work in a fast-paced, collaborative environment with a growth mindset.

• Strong communicationand documentation skills.

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.

Responsibilities

• optimizetraining and inference pipelines for LLMs, SLMs, multimodal, and code-specific models.

• 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.