Software Engineer
Microsoft
Software Engineer
Taipei, Taipei City, Taiwan
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Overview
Microsoft’s vision is to democratize AI to enable every person and every organization on the planet to achieve more. With a growth mindset, we innovate to empower others, collaborate toward shared goals, and uphold respect, integrity, and accountability—fostering a culture of inclusion where everyone can thrive.
The Azure MultiModal Intelligence (MMI) team drives the development of advanced cognitive services across documents, video, image, and audio, powering scenarios such as retrieval-augmented generation (RAG), robotic automation processing (RAP), knowledge retrieval, agentic services and many more. Within this broad charter, we are seeking engineers to focus on advancing document intelligence—building best-in-class cloud and on-premises solutions that leverage deep learning and Large Language Models (LLMs) to help businesses automate document processing intelligently with AI.
Come join a creative and dedicated team of engineers! You’ll gain first-hand experience building AI products that are compliant, secure, reliable, and high-performing—serving millions of requests worldwide.
To learn more about our team, check out this document.
Qualifications
Qualification
- Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, Physics or related fields
- 1+ years of strong product development and engineering experiences covering various aspects of AI/ML system design and management.
- Experience in Natural Language Processing (NLP), Large Language Model (LLM), Semantic Parsing, Transfer Learning.
Preferred Qualification
- Familiarity with cloud platforms (Azure, AWS, or GCP) and distributed systems.
Responsibilities
We are looking for strong software engineers who can be expected to learn ML/LLM/Data Science concepts on the job. Key responsibilities include:
- Build end-to-end AI solutions to bring models from proof-of-concept to production.
- Leverage Azure services (e.g., Azure ML, Azure DevOps, etc.) and develop customized modules to instrument the MLOps practice.
- Support and manage machine learning models, production pipeline, and experimentation platform.
- Work closely with data engineers, service engineers, and data program manager in the cycle of productizing AI models.