Senior Researcher

About the Role

Microsoft Research India is seeking a Senior Researcher to advance the state of the art in how LLM-based agents learn and adapt. This role focuses on building self-evolving agentic systems that improve their own policies, skills, memory, and scaffolding through interaction, feedback, and reinforcement learning. You will work across the full stack, from designing reinforcement learning algorithms and reward signals to building environments and evaluations, translating research ideas into systems that operate at scale. Collaboration with researchers and engineers across Microsoft Research India, worldwide, and product groups is key, enabling contributions to both the research community and widely used products.

Key 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.

Requirements & Tech Stack

• Passion for advancing the state of the art in reinforcement learning, LLM post-training, and autonomous agents.
• Strong research track record demonstrated through publications at leading venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, COLM) or impactful open-source contributions.
• Combination of scientific rigor with strong engineering skills.
• Ability to thrive in a highly collaborative research environment.
• Aptitude for taking end-to-end ownership of complex, open-ended research problems.

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.