Microsoft Member Technical Staff AI Reinforcement Systems 

Taiwan, Taoyuan City 

464182389

We're on a mission to create trustworthy agents capable of autonomous action and decision-making on behalf of our users. As part of our team, you’ll help advance state-of-the-art model capabilities by contributing to core systems, infrastructure, and research.

We are looking for distributed systems experts with a scientific mindset. The ideal candidate will be able to build complex systems from the ground up, discover and diagnose causes of suboptimal performance, and contribute to solving scientific and research challenges. Specifically, they should:

• Excel in programming (especially parallel/concurrent), software engineering, and API design

• Have experience in large-scale systems, preferably having built some components from scratch.

• Thrive in a highly collaborative, fast-paced environment

• Have a high degree of craftsmanship and pay close attention to details

• Effectively manage multiple responsibilities and can adjust to shifting priorities

• Be motivated by training capable and safe AI agents and shipping them into the hands of millions of users

A background in machine learning is preferred but not required. In this case, candidates must demonstrate they have an ability to quickly learn the subject, and backgrounds in mathematics, competitive programming, and related domains are a plus.

Required Qualifications:

• Bachelor's Degree in Computer Science, Software Engineering, Computer Engineering, Machine Learning, Mathematics, or related STEM fields and experience in coding in languages including, but not limited to, C, C++, C#, Rust, Java, or Python
• OR equivalent experience.
• Experience with large-scale software systems and infrastructure.
• Demonstrated interest in reinforcement learning, language modelling, generative modelling, or related domains.

Preferred Qualifications:

• Background in machine learning research.
• Experience with large scale distributed AI systems.
• Ability to work collaboratively in a fast-paced, innovative environment.

• Collaborate with research teams to advance state-of-the-art algorithms for reinforcement learning in LLMs
• Develop the core systems for adapting reinforcement learning to unprecedented scales and heterogeneous environments.
• Embody our culture of collaboration, innovation, and excellence.