Applied AI Research Internship 2027 (Yorktown Heights, Cambridge)

Job Description

Join the Software Innovation Lab team at IBM Software and help shape the future foundations of artificial intelligence and data. Our scientists and engineers conduct end-to-end research that delivers real-world AI impact through a rigorous, responsible, and open innovation framework. We are the R&D team driving innovation across IBM Software, and the technologies we build are relied on by IBM customers today and in the future.

As an intern, you will explore cutting-edge research areas, with a focus on AI models and approaches for a real-time data and context backbone for agents and stream-native AI. Topics include hybrid world models, real-time context optimization for agents and models, and semantic stream processing for stored and streaming data. You will study efficiency, accuracy, and scalability challenges, test new theories, and design experiments to validate novel computational models.

As an AI Research Scientist Intern, you will take part in the full research lifecycle. Your work will involve identifying core challenges, designing prototype solutions, and validating them through rigorous experimentation. Key research areas include stream-native AI services for agents, multi-agent synthetic skill synthesis and reuse with coding agents, transforming event streams into hybrid world models, and query and context optimization for integrated streamed and stored data.

You will work in small, mentored teams and be responsible for taking projects from ideation to completion. A key objective is to publish significant results in leading conferences and file patent applications, contributing to the academic community and IBM's open innovation initiatives.

Program Dates

• Semester System: May 24, 2027 to August 13, 2027
• Quarter System: June 14, 2027 to September 3, 2027

Start dates vary based on your academic calendar. Candidates must be available for the full duration of the internship.

Requirements

• Research Experience with Modern ML: Hands-on experience with and a deep theoretical understanding of Large Language Models (LLMs) and transformer-based architectures.
• Strong Programming and Prototyping Skills: Proficiency in Python for rapid prototyping, experimental setup, and implementing large-scale machine learning systems.
• Analytical and Problem-Solving Abilities: Demonstrated strength in quantitative analysis, designing experiments, and deconstructing complex research problems.
• Research and Communication Skills: A track record of innovation, evidenced by peer-reviewed publications or strong preprints, and the ability to articulate complex technical concepts in writing and presentations.
• Collaborative Research Mindset: A team-oriented approach with a commitment to rigorous, reproducible, and well-documented research practices.

Preferred Qualifications

• Research Excellence: Strong publication record in top-tier conferences such as NeurIPS, ICML, ICLR, VLDB, SIGMOD, and 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), from training to testing, applied to data management challenges. Experience with stream processing is a plus.
• Communication: Demonstrated ability to present complex research and build high-impact technical demonstrations.

Location Flexibility: By applying, you agree to be considered for any listed location and to work at the location where you are ultimately assigned.