Senior Researcher - Foundations of Generative Ai- Microsoft Research

Microsoft Microsoft · Big Tech · New York, NY +1 · Research Sciences

Senior Researcher focused on the foundations of generative AI, including new architectures, representations, and learning objectives for foundation models and learning agent platforms. Research areas include proactive agents, test time training, active visual reasoning, world models, multi-scale temporal reasoning, continual learning, multimodal models (VLM, VLA), and real-time agents.

What you'd actually do

  1. Apply research and engineering skills to develop, prototype, and evaluate cutting-edge research ideas.
  2. Work closely with other researchers and engineers to rapidly prototype and test new research ideas, driving a high-impact agenda and publishing results where appropriate.
  3. Collaborate hands-on with other researchers, engineers, and internal and external product groups to deliver high-impact solutions to real-world problems.

Skills

Required

  • Doctorate (or currently pursuing) in Computer Science or relevant field
  • Python
  • PyTorch
  • TensorFlow
  • HuggingFace

Nice to have

  • 2+ years related research experience
  • publications at top AI conferences (NeurIPS, ICML, ICLR, ACL, NAACL, CVPR, COLT, ECCV, ICCV, EMNLP)
  • Deep understanding of frontier model architectures, especially transformers and state space models
  • pre-training
  • fine-tuning
  • inference
  • building and deploying prototypes, applications, or open-source (OSS) technologies
  • GitHub profile and/or code samples
  • Ability to work independently
  • Ability to collaborate, communicate effectively
  • Keen interest in real-world applications and impact

What the JD emphasized

  • Doctorate (or currently pursuing) in Computer Science or relevant field
  • 2+ years of academic or industry experience in developing, applying, and/or implementing algorithms for machine learning/statistics, using common ML engineering programming languages and platforms such as Python, Python numerical libraries, PyTorch, TensorFlow and/or HuggingFace.
  • Experience publishing academic papers as a lead author or essential contributor in a top AI conference or journal.
  • Hands-on experience building and working with Large Language Models (LLMs) or multimodal models (VLMs, VLAs), including pre-training, fine-tuning, and inference

Other signals

  • foundation models
  • learning agent platforms
  • new architectures
  • representations
  • learning objectives