Research Intern, Semantic Scholar

Hybrid: Persons in these roles are expected to spend part of their time on-site in our Seattle offices and may occasionally work remotely from their home in the Greater Seattle area. On-site requirements vary based on position and team. If you have questions about Hybrid work arrangements for this role, please ask your recruiter.

Compensation Range: $86,520 - $123,600

Who You Are:

Are you passionate about taking artificial intelligence to the next level, to solve problems that require modeling, reasoning, and explanation? We are looking for talented researchers interested in applying their skills towards the next generation of intelligent systems, and pursuing our mission of AI for the common good.

As a Research Intern on our team, you’ll have the unique opportunity to work on research problems that can improve the productivity and discovery experience of scientists through the development of intelligent research assistants and platforms like Semantic Scholar (semanticscholar.org) . 

We collaborate frequently with researchers on other Ai2 teams focused on natural language processing and computer vision, among others. Our main office has close ties and unique access to researchers at the University of Washington, only 1.5 miles away. Researchers excited about such internal and external collaborations are encouraged to apply.  

All students with an interest in researching how AI can accelerate science are encouraged to apply, and in the current hiring cycle we are especially interested in the following areas:

• Human-AI collaboration
• Model distillation and specialization
• Agentic planning, reasoning, and learning

See our publications for examples of our work. 

We will continue to consider applications on a rolling basis but for full consideration for start dates in summer 2025, please apply by November 1, 2024.

Ai2 Research Internship Information:

• Duration: 12 weeks
• Start date: Flexible
• Candidates: A PhD candidate or a master/undergraduate student with a strong research background

We are currently hiring for start dates in 2025. If you have questions about the hiring process or timeline, please consult our team hiring FAQs or reach out to s2-research-careers@allenai.org with further questions.

Who We Are: 

Semantic Scholar Research at the Allen Institute for AI (Ai2) is an interdisciplinary research team focused on AI, HCI, ML, NLP, accessibility, and computational social science in support of our mission of accelerating science. Our research powers open platforms and tools that assist scholars in performing scientific research tasks (e.g., literature discovery and synthesis, hypothesis generation, etc.) such as the Semantic Scholar platform and intelligent research assistants.  We regularly publish in high-profile conferences and journals in NLP (e.g., ACL, EMNLP), HCI (e.g., CHI, UIST, CSCW), ML (e.g., NeurIPS, ICLR), IR (e.g., SIGIR), Accessibility (ASSETS), and other areas.

Your Next Challenge:

Research interns on our team will have the opportunity to:

• Conduct high-impact research and author scientific papers for publication in high-profile computing conferences and journals,
• Collaborate with and learn from a dedicated Ai2 mentor and other team members across Ai2, including scientists and engineers,
• Implement and share open source software to improve access to our research, and
• Help develop collaborative relationships with relevant academic, industrial, government, and standards organizations.

What You’ll Need:

• Are a student completing a PhD or a Master/Undergraduate with research experience in Computer Science or related field, with specific focus in one or more of our team’s active research areas.
• A strong publication record in AI-related areas.  Example venues include ACL, CHI, CSCW, ICLR, EMNLP, SIGIR, KDD, WWW, CVPR, NeurIPS, ICML, and UIST.  Contributions to research communities (e.g. workshop organization, tutorials) are a plus.
• Strong software engineering skills.  Experience with system building (e.g., web application frameworks) or deep learning frameworks (e.g. PyTorch, Tensorflow). Contributions to open-source research libraries (e.g. AllenNLP, spaCy) are a plus.

Physical Demands and Work Environment:

The physical demands described here are representative of those