Researcher Intern - CoreAI Post Training

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Come build community, explore your passions and do your best work at Microsoft with thousands of university interns from every corner of the world. This opportunity will allow you to bring your aspirations, talent, potential – and excitement for the journey ahead.

The Microsoft CoreAI Post-Training team advances post-training methods for both OpenAI and open-source models. We work on continual pre-training, large-scale deep RL on large GPU fleets, data curation/synthesis at scale, and practical fine-tuning for research and product. We also build language + multimodal technologies used across Microsoft, with a special focus on code-centric models for GitHub Copilot and Visual Studio Code (completion and SWE agent models). Our work connects to efforts such as LoRA, DeBERTa, Oscar, Rho-1, Florence, and the open-source Phi family.

We prize research innovation and bold experimentation—aiming for breakthroughs that materially advance the state of the art and ship into products.

As an intern at Microsoft, you’re stepping into a world of real impact from day one. You’ll collaborate with global teams on meaningful projects, explore cutting-edge technologies like AI, and kick start your career while doing it. With a strong focus on learning and development, this is your opportunity to grow your skills, build community, and shape your future—all while being supported every step of the way.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate and empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Required Qualifications:

• Currently enrolled in a BS/MS/PhD program in computer science, AI/ML, data science, electrical engineering, or a related field
  
• Must have at least one additional quarter/semester of school remaining following the completion of the internship.
• Candidate must be enrolled in a full time bachelor's, masters, MBA, or PhD program in area relevant for the role during the academic term immediately before their internship.
• Effective coding skills in Python and modern data/ML libraries (NumPy, Pandas, PyTorch/JAX/TF)
• Familiarity with training/evaluating ML models and with basic data-pipeline concepts
  

Preferred Qualifications:

• First-author publication(s) at top-tier AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) or equivalent journals; or demonstrably comparable research impact (e.g., widely used open-source, SOTA results, benchmark wins)
  
• Experience with distributed data or training frameworks (Spark, Ray, Beam; PyTorch DDP/FSDP) and cloud ecosystems (Azure; data lakes)
  
• Exposure to large-scale, un/semi-structured datasets (images, video, audio, code)
  
• Prior work on LLMs, RL/RLHF, post-training, or multimodal models
  
• Contributions to open-source tooling or reproducible research
  
• Clear communication, self-motivated, curiosity, and a bias for hands-on experimentation

The base pay range for this internship is USD $5610.00 - $11010.00 per month. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $7270.00 - $12030.00 per month.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-intern-pay

Microsoft accepts applications and processes offers for these roles on an ongoing basis throughout the academic calendar (September - April)

#Research #EiP #CoreAI #AIPlatform

• Design & evaluate datasets: build high-quality datasets/benchmarks; run ablations to measure impact and improve data effectiveness
• Advance model training: contribute to pre-training, post-training, and RL for language and multimodal models
• Develop data infrastructure: extend pipelines for ingest, preprocess, filter, and annotate large, heterogeneous data
• Data quality & analysis: assess text, image, video, audio, and code data for quality, diversity, and relevance; propose improvements
• Tooling & workflows: create lightweight tools for dataset auditing, visualization, and versioning to speed iteration
• Research & collaboration: work with researchers/engineers to push research and product boundaries with measurable impact