Principal Researcher - Agentic AI - Microsoft Research AI Frontiers

The mission of the AI Frontiers lab is to expand the pareto frontier of Artificial Intelligence (AI) capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. Some of our projects include work on Small Language Models (e.g. Phi, Fara, Fara-multimodal, OmniParser) and agentic AI systems (e.g. AutoGen, MagenticOne, Magentic-UI).

We are advancing the science of agents with research on Machine Learning (ML), Human-Computer Interaction (HCI) and AI. To bridge foundational breakthroughs with real-world agent autonomy, we are building an end-to-end agentic model stack that integrates pre-training, post-training, reinforcement learning (RL), multi-agent collaboration, agentic harness, and deployment. Our core objective is to create self-improving AI systems that can navigate dynamic enterprise environments by learning through experience and interaction.

The Role

As a Principal Researcher - Agentic AI, you will play a crucial role in modeling agentic intelligence: developing agents that learn to self-correct, specialize and collectively improve through self-play as well as interaction. The role spans research on recursive selfimprovement using selfgenerated data, adaptive coordination and delegation across multiple models, continual learning without catastrophic forgetting, human-AI collaboration for long-horizon tasks, and autonomous skill acquisition driven by interactionidentified gaps.

This role encompasses the full research-to-deployment spectrum, requiring an active focus on building robust evaluation frameworks and high-fidelity synthetic data pipelines. You will also be instrumental in refining the underlying architectures to ensure agentic systems scale effectively in complex environments.

Environment & Focus

Researchers in the AI Frontiers lab work in a vibrant, multidisciplinary environment with access to large scale pipelines and evaluation environments, the ability to ground research in real world product scenarios, and an open publication policy designed to share breakthroughs in agentic autonomy with the global research community.

Our current research focuses on the intersection of self-improving systems and multi-agent reasoning, specifically exploring how recursive improvement, adaptive coordination, and autonomous skill acquisition can enable agents to continuously evolve through interaction without losing prior capabilities.

At Microsoft, our mission-to empower every person and every organization on the planet to achieve more-guides how we partner with customers to deliver trusted, impactful solutions. With a growth mindset culture, we innovate responsibly and measure success by shared progress-people, teams, and customers. Join us to do meaningful work that changes the world and helps shape what's next for everyone.

Required Qualifications

• Doctorate in Computer Science or relevant field AND 3+ years related research experience
  • OR Master's Degree in Computer Science or relevant field AND 4+ years related research experience
  • OR Bachelor's Degree in Computer Science or relevant field AND 6+ years related research experience
  • OR equivalent experience.

Preferred Qualifications

• Experience with building and evaluating agentic models.
• Research record demonstrated by public artifacts like models, tools, code in AI space or publications at the following conferences: NeurIPS, ICML, ICLR, ACL, NAACL, COLM.
• Expertise in the technical scaling of models and a commitment to engineering excellence, including the development of generalized code and robust research infrastructure.
• Experience publishing academic papers as a lead author or essential contributor in the field of Artificial Intelligence, deep learning, natural language processing and/or reinforcement learning.
• Experience participating in a top conference in relevant research domain (i.e. organizing a workshop, hackathon, community engagement/relations).
• Demonstrable ability to define an ambitious, original research agenda.
• Ability to collaborate, communicate effectively, and technically lead multi-disciplinary team.
• Keen interest in real-world applications and impact.

#MSR #Research

Research Sciences IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

Research Sciences IC6 - The typical base pay range for this role across the U.S. is USD $163,000 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

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.