Research Software Development Engineer-msr

Microsoft Microsoft · Big Tech · Bengaluru, KA, IN · Software Engineering

Microsoft Research India is seeking research engineers with a strong track record in AI/ML to work on new models, scalable ML architectures, systems support, and large-scale applications. The role involves close collaboration with researchers and engineers, driving end-to-end research and development from ideation to deployment. Preferred qualifications include understanding of deep learning, LLMs, large-scale ML systems, and experience in data preparation, pre-training, post-training, and evaluation.

What you'd actually do

  1. working closely with researchers, engineers and internal/external partners and take ownership of meeting project goals through building quality solutions
  2. receive mentoring as well as to mentor and lead interns and other team members
  3. driving end-to-end research and development—from ideation to deployment
  4. dealing with open problems, new technologies and changing requirements
  5. play different roles in the project as the need arises

Skills

Required

  • Bachelor’s, Master’s, or Ph.D. degree in Computer Science or related disciplines
  • Strong programming and communication skills
  • ability to drive end-to-end research and development—from ideation to deployment
  • Minimum 5 years of industry work experience

Nice to have

  • Good understanding of deep learning, LLMs, and large-scale ML systems
  • Proficiency in Python, PyTorch
  • familiarity with CUDA, cutting-edge agentic frameworks, etc.
  • Experience in data preparation, pre-training, post-training, and evaluation for ML models

What the JD emphasized

  • proven track record of innovation and delivering impactful solutions
  • strong coding and engineering skills
  • deep learning
  • LLMs
  • large-scale ML systems
  • data preparation
  • pre-training
  • post-training
  • evaluation for ML models
  • agentic frameworks

Other signals

  • research engineers
  • AI/ML
  • new models
  • scalable ML architectures
  • systems support for such workloads
  • innovative large-scale applications of ML models
  • deep learning
  • LLMs
  • large-scale ML systems
  • data preparation
  • pre-training
  • post-training
  • evaluation for ML models
  • agentic frameworks