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Microsoft Corporation Logo Microsoft Corporation · IT - Software Development

Applied Scientist II

India, Bangalore · Job Posted June 29, 2026
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Job Description

Frontier Tuning aims to fine-tune frontier LLMs on enterprise data, enabling task-specific agents and solutions. We are a small, nimble team that is advancing the state of the art of models in M365 Copilot. Come join our team and help transform the LLM experience in the enterprise.

Job Responsibility

  • Train and deploy Language Models adapted to specific industry needs
  • Create and adapt novel training and fine-tuning algorithms for language models with special focus on reinforcement learning for long-horizon dynamic workflow
  • Research innovation and scholarly dissemination: conceive and execute research projects that advance training methodologies
  • write and submit peer-reviewed papers or preprints
  • and present work at conferences
  • Drive end-to-end translation of research into product capabilities, leading projects from ideation and prototyping through production integration and measurable customer impact

Requirements

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • OR equivalent experience
  • 1+ years of experience training/fine tuning AI/ML models, preferably large language models
  • 1+ years of experience building Generative AI pipelines, e.g. with RAG
  • 1+ years of experience with Python and/or PyTorch
  • Ability to meet Microsoft, customer and/or government security screening requirements
  • Microsoft Cloud Background Check

Nice to have

  • Experience with multi-agent training in dynamic harness
  • Experience training or contributing to the development of very large-scale language models (e.g., 100B+ to trillion-parameter models), including distributed training, async RL, and long sequence handling
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