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Research Scientist - R&D Internship – 2027 Yorktown Heights & Cambridge

IBM2,538 open roles

Where
Multiple Cities
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Your applicationOpen nowResearch Scientist - R&D Internship – 2027 Yorktown Heights & CambridgeIBM · Multiple Cities

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The clock on this job

Early applications get read.

8.2% of postings close within 7 days. Measured by our own scanner across the market. IBM postings stay open a median of 6 days.

Share of postings closed within
  1. 1.9%1 day
  2. 3.8%3 days
  3. 8.2%7 days
  4. 15.2%14 days
  5. 34.1%30 days
This job: posted today

IBM median: 6 days open

The posting

Join the pioneering Software Innovation Lab team at IBM Software and contribute to shaping the future foundations of artificial intelligence. Our group of scientists and engineers are dedicated to conducting end-to-end research that delivers real-world AI impact through a rigorous, responsible, and open innovation framework. We are the R&D team that is the innovation engine of IBM Software. We are shaping the technologies that IBM customers rely on – today and in the future. As an intern, you will advance state-of-the-art agent architectures and domain-specific models for business processes, IT and industrial automation, and software engineering across distributed and non-distributed environments, while developing responsible AI systems and enterprise-grade benchmarks that help enterprises personalize services, automate operations, optimize workflows, predict demand, and deliver effective outcomes. With a culture built on curiosity, creativity, and collaboration, this role offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world.

As an AI Research Scientist Intern, you will engage in the full research lifecycle to pioneer new advancements in artificial intelligence. Your role will involve identifying core challenges, designing novel prototype solutions, and validating them through rigorous experimentation. You will help advance our research in foundation models and multimodal AI, business process intelligence and automation, IT and industrial automation, AI for software engineering, reliable and scalable agentic systems, AI security and trustworthiness, enterprise knowledge graphs, continual agent learning and adaptation, and knowledge-guided workflow orchestration. Collaborating in small, mentored teams, you will be responsible for shepherding projects from ideation to completion. A key objective is to disseminate significant results through publications in leading conferences and patent applications, contributing to both the academic community and IBM's open innovation initiatives.

Program Dates: Internship start dates vary based on your academic calendar. • Semester System: May 24, 2027 – August 13, 2027 • Quarter System: June 14, 2027 – September 3, 2027 Candidates must be available to participate for the full duration of the internship corresponding to their academic schedule.

Location Flexibility: By applying to this requisition, you acknowledge and agree to be considered for any of the listed locations associated with this position and are willing to work at the location where you are ultimately assigned.

  • Research Experience in Modern AI and Systems: Hands-on experience and theoretical foundations in one or more of the following areas: foundation and multimodal models, agentic systems, AI for software engineering, knowledge graphs, continual learning, process intelligence, or distributed systems.
  • Strong Programming & Prototyping Skills: Proficiency in Python for rapid prototyping, experimental setup, and implementing large-scale machine learning systems.
  • Rigorous Analytical & Problem-Solving Abilities: Demonstrated strength in quantitative analysis, designing experiments, and deconstructing complex research problems.
  • Proven Research & Communication Skills: A track record of innovation (evidenced by peer-reviewed publications or strong preprints) and the ability to clearly articulate complex technical concepts in both writing and presentations.
  • Collaborative Research Mindset: A team-oriented approach with a commitment to rigorous, reproducible, and well-documented research practices.
  • Research Excellence: Strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, AAAI).
  • Technical Proficiency: Advanced expertise in ML frameworks (PyTorch) and full-cycle development of algorithms and systems.
  • Specialized Skills: Hands-on experience with generative AI (LLMs) and multimodal models, from training to testing.
  • Communication: Demonstrated ability to present complex research and build high-impact technical demonstrations.
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