Senior Researcher - AI Agents - Microsoft Research

Microsoft Corporation•Mountain View, CA

29d

About The Position

Microsoft Research (MSR)â¯AI Frontiersâ¯is inviting applications for aâ¯Seniorâ¯Researcher to join our team. At MSR AI Frontiers, we pursue ambitious research to advance AI in areas such as modeling, algorithms, agents, and agentic systems. We foster a vibrant environment for multidisciplinary work with an open publication policy, close ties to leading academic institutions, and a commitment to impact beyond research. We regularly release open-source models, libraries, and tools to accelerate community progress, while also working within Microsoft's ecosystem to ship AI technologies across multiple products, ensuring our innovations create real-world value for people. We are seeking exceptional candidates to advance the state-of-the-art in agentic systems and ecosystems. Relevant projects from our lab include agentic systems (e.g., Magentic-UI, Magentic-One), ecosystems (e.g., Magentic Marketplace), frameworks (e.g., AutoGen), models (e.g., Phi, Orca) and tools (e.g., AgentInstruct, AutoGen Studio). Focus areas for this position include, but are not limited to, the following: Agentic Systems. Building state-of-the-art agentic systems, applications, and environments. Agent Ecosystems. Building agent-native ecosystems (e.g., frontier firms, agentic markets) where agents can effectively collaborate alongside people and other agents. Advanced Agentic Capabilities. Post-training models for advanced agentic capabilities, including reinforcement learning, fine-tuning, self-play, and synthetic data generation. Human-Agent Interaction. Creating novel human-agent interaction techniques and experiences. Evaluation & Tooling. Developing evaluation benchmarks, methods, and tooling for agentic capabilities. Responsible AI. Ensuring agents operate safely and responsibly while delivering real value to people. As a Senior Researcher on our team, you will work hands-on on challenging and impactful projects. You will apply your strong technical skills and expertise to build practical solutions to real world problems. Additionally, you will collaborate closely with other researchers and engineers across the company to amplify your impact and grow your career in a supportive and stimulating environment.

Requirements

Doctorate (or currently pursuing) in Computer Science or related fields
OR equivalent experience

Nice To Haves

Publication record as a lead author or essential contributor at top venues such as: CHI, NeurIPS, UIST, ICML, ICLR, ACL, EMNLP, CVPR, AAAI, ICAPS.
Doctorateâ¯inâ¯Computer Scienceâ¯or relevant fieldâ¯ANDâ¯2â¯years related research experience
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. Providing a link to GitHub profile (if available) and/or code samples, is highly encouraged.
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.
Reference letters are welcomed, though they are not mandatory. Please include them if available.

Responsibilities

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.
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.
Problem Solving- The ability to identify problems and review related information to develop and evaluate options and implement solutions.
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).
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.

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