Principal Researcher - Agentic AI - Microsoft Research AI Frontiers

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Research Sciences

Principal Researcher focused on expanding AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. The role involves developing self-improving, adaptive agents for dynamic enterprise environments, integrating pre-training, post-training, RL, multi-agent collaboration, and deployment. Key responsibilities include research on recursive self-improvement, continual learning, human-AI collaboration, and building robust evaluation frameworks and synthetic data pipelines.

What you'd actually do

  1. Perform cutting-edge research in collaboration with other researchers, engineers, and product groups.
  2. Be a part of research breakthroughs in the field and will be given an opportunity to realize your ideas in products and services used worldwide.

Skills

Required

  • 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.

Nice to have

  • building and evaluating agentic models
  • models, tools, code in AI space or publications at the following conferences: NeurIPS, ICML, ICLR, ACL, NAACL, COLM
  • technical scaling of models
  • engineering excellence
  • generalized code
  • robust research infrastructure
  • 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
  • participating in a top conference in relevant research domain (i.e. organizing a workshop, hackathon, community engagement/relations)
  • define an ambitious, original research agenda
  • collaborate, communicate effectively, and technically lead multi-disciplinary team
  • Keen interest in real-world applications and impact

What the JD emphasized

  • expanding the pareto frontier of Artificial Intelligence (AI) capabilities, efficiency, and safety
  • building an end-to-end agentic model stack
  • create self-improving AI systems
  • modeling agentic intelligence
  • recursive self‑improvement
  • adaptive coordination
  • continual learning
  • autonomous skill acquisition
  • building robust evaluation frameworks
  • high-fidelity synthetic data pipelines
  • agentic systems scale effectively
  • self-improving systems
  • multi-agent reasoning
  • recursive improvement
  • adaptive coordination
  • autonomous skill acquisition
  • continuously evolve through interaction without losing prior capabilities

Other signals

  • expanding the pareto frontier of Artificial Intelligence (AI) capabilities, efficiency, and safety
  • innovations in foundation models and learning agent platforms
  • building an end-to-end agentic model stack that integrates pre-training, post-training, reinforcement learning (RL), multi-agent collaboration, agentic harness, and deployment
  • create self-improving AI systems that can navigate dynamic enterprise environments by learning through experience and interaction
  • modeling agentic intelligence: developing agents that learn to self-correct, specialize and collectively improve through self-play as well as interaction
  • recursive self‑improvement using self‑generated data
  • adaptive coordination and delegation across multiple models
  • continual learning without catastrophic forgetting
  • human-AI collaboration for long-horizon tasks
  • autonomous skill acquisition driven by interaction‑identified gaps
  • building robust evaluation frameworks and high-fidelity synthetic data pipelines
  • refining the underlying architectures to ensure agentic systems scale effectively in complex environments
  • intersection of self-improving systems and multi-agent reasoning