Senior Researcher - AI Agents - Microsoft Research

Microsoft Microsoft · Big Tech · Redmond, WA +3 · Research Sciences

Senior Researcher focused on advancing state-of-the-art in agentic systems and ecosystems, including building agent-native ecosystems, developing advanced agentic capabilities through post-training methods, creating novel human-agent interaction techniques, and developing evaluation benchmarks and tooling for agents. The role emphasizes outcome-driven innovation, collaborative innovation, problem-solving, decision-making, and the scientific method, with a strong publication policy and potential for shipping AI technologies.

What you'd actually do

  1. Outcome Driven Innovation- The ability to strategically recognize and address unmet needs in industry or knowledge and to create novel, practical, and effective solutions to these unmet needs. This involves the ability to work backwards in research and development, focusing more heavily on the problem to lead to a solution rather than creating something highly original but lacking in application potential.
  2. Collaborative Innovation- Knowledge of others' expertise and the ability to involve multiple players (within and outside the organization) in the creation or development of novel products, processes, or research streams.
  3. Problem Solving- The ability to identify problems and review related information to develop and evaluate options and implement solutions.
  4. Decision Making- The ability to make decisions in a fast-paced, rapidly changing environment. This includes the ability to define, diagnose, and determine an appropriate resolution, recommendation, or decision while considering alternatives and factors (e.g., resources, costs, tradeoffs).
  5. Scientific Method- Knowledge of and the ability to use an empirical method of acquiring knowledge. This involves careful observation, applying rigorous skepticism about what is observed, formulating a question or hypothesis, testing the hypothesis through experimentation, drawing conclusions, reporting results, and evaluating the process and retesting the hypothesis.

Skills

Required

  • Doctorate (or currently pursuing) in Computer Science or related fields or equivalent experience
  • Hands-on experience working with large foundation models (e.g., OpenAI GPT models, LLAMA etc) and state-of-the-art AI/ML frameworks and toolkits (e.g., Pytorch, Tensorflow, Langchain, AutoGen)
  • Demonstrated software engineering experience building and deploying prototypes, applications, or open-source technologies
  • Hands-on experience evaluating AI models or systems (e.g., running benchmark experiments or user studies, analyzing data)
  • Experience working in multi-disciplinary teams along with a team player mindset, characterized by effective communication, collaboration, and feedback skills

Nice to have

  • Publication record as a lead author or essential contributor at top venues such as: CHI, NeurIPS, UIST, ICML, ICLR, ACL, EMNLP, CVPR, AAAI, ICAPS
  • Doctorate in Computer Science or relevant field AND 2 years related research experience

What the JD emphasized

  • agentic systems
  • agent ecosystems
  • advanced agentic capabilities
  • human-agent interaction
  • evaluation & tooling
  • responsible AI
  • publication record
  • foundation models
  • AI/ML frameworks
  • software engineering experience
  • evaluating AI models or systems

Other signals

  • agentic systems
  • agent ecosystems
  • advanced agentic capabilities
  • human-agent interaction
  • evaluation & tooling for agents
  • responsible AI for agents