We are looking for candidates who
Are passionate about advancing the state of the art in reinforcement learning, LLM post-training, and autonomous agents.
Have a strong research track record demonstrated through publications at leading venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, or similar venues, or through impactful open-source contributions.
Combine scientific rigor with strong engineering skills and enjoy turning research ideas into working systems.
Thrive in a highly collaborative research environment.
Take end-to-end ownership of complex, open-ended research problems.
Qualifications
Responsibilities
Lead research on self-evolving agents, including self-improvement loops, experience-driven learning, self-generated curricula, and automatic refinement of agent policies, tools, and memory.
Design and apply reinforcement learning methods for LLM-based agents, including reward modeling, learning from verifiable and AI feedback, and credit assignment over long, multi-step tasks.
Build scalable training and evaluation environments for agentic tasks such as coding, tool use, web and computer use, and knowledge work.
Develop rigorous benchmarks and evaluation methods that measure agent capability, robustness, safety, and improvement over time.
Form hypotheses, design experiments, and iterate quickly on model and agent performance.
Publish at top-tier venues, release open-source artifacts, and help shape the research agenda in agentic AI.
Collaborate with research and product teams across Microsoft to move research advances into real-world systems.
Mentor interns and early-career researchers and contribute to Microsoft Research India’s academic engagement.
Embody our culture and values.
Required Qualifications
PhD in Computer Science, Machine Learning, Mathematics, or a related technical field, or equivalent experience.
1+ years of software engineering or research experience.
Experience with reinforcement learning and LLMs.
Strong research track record in machine learning, computer systems, or a closely related area.
Demonstrated ability to conduct independent research and communicate results through publications, prototypes, or other impactful outcomes.
Ability to work effectively across disciplinary boundaries and collaborate with diverse research and engineering teams.
Preferred Qualifications
Experience building and scaling large AI systems.
Experience applying reinforcement learning to AI models and agents.
Experience with reward modeling, reinforcement learning, or other post-training techniques.
Hands-on experience building datasets for LLM, VLM, or multimodal model pre-training or post-training.
Experience with synthetic data, model graders, reward signals, hard-example mining, active learning, or data-mixture optimization.
Experience building feedback loops that connect evaluation, failure mining, data generation, and training.
Experience with multimodal or agentic datasets, including text, web, GUI, tool-use, or interaction trajectories.
How to Apply
Please apply using the application link above. In addition, please send an email to the Hiring Committee at seas-hiring-msri@microsoft.com.
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.
