Member of Technical Staff, AI Reinforcement Systems

5 Months ago • All levels

Research Development

Job Description

Microsoft AI is seeking a Member of Technical Staff to build advanced reinforcement learning systems. Responsibilities include collaborating with research teams to improve reinforcement learning algorithms for LLMs, developing core systems for adapting reinforcement learning to large scales and diverse environments, and contributing to core systems, infrastructure, and research. The ideal candidate excels in programming (parallel/concurrent), software engineering, and API design, has experience with large-scale systems, thrives in collaborative environments, and has a high attention to detail. A background in machine learning is preferred but not required; strong mathematical or competitive programming skills are valuable substitutes. The role involves managing multiple responsibilities and adapting to changing priorities, ultimately aiming to deliver safe and capable AI agents to millions of users.

Good To Have:

Machine learning research background
Experience with large-scale distributed AI systems
Strong mathematical or competitive programming skills

Must Have:

Experience with large-scale software systems
Proficient in programming (parallel/concurrent)
Expertise in software engineering and API design
Excellent collaboration and communication skills

Add these skills to join the top 1% applicants for this job

team-management

excel

cpp

rust

reinforcement-learning

python

algorithms

css

java

machine-learning

Overview

Help build the world’s most advanced reinforcement learning systems at Microsoft AI.

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.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Qualifications

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.
Experience 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.

Responsibilities

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.

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