The Teachable AI Experiences (TAIX) team at Microsoft Research Cambridge (UK) is a multi-disciplinary team that aims to drive technical innovation in human-AI interaction through the lens of inclusion. The team is looking to hire an intern with expertise in human-computer interaction (HCI) to direct and contextualize machine learning research into how we improve the data ecosystems for large multi-model models to ensure that they are more inclusive.
The goal of the internship will be to develop participatory approaches for data augmentation of models. The candidate should have: 1) experience in participatory methods; 2) good literature review skills; 3) experience with machine learning in some capacity; and 4) a grounding in inclusion or fairness research.
Candidates should have a passion for equitable AI. The outcomes of the project may lead to a publication in a relevant conference and/or integration into a Microsoft product or application.
The internship offers a unique opportunity to have real-world impact and drive state-of-the-art research at the intersection of ML and human-computer interaction, in collaboration with a multi-disciplinary team.
Qualifications
Required/minimum qualifications
- Currently pursuing a PhD in human-computer interaction or a related area. All applicants must be currently enrolled in an educational institution.
- Demonstrable experience in participatory methods
- Demonstrable ability to drive high-quality research insights through publications in top-tier conferences and journals
- Effective communication skills and ability to work in a collaborative environment.
Preferred/additional qualifications
- The ability to approach technical problems and design solutions with a multi-disciplinary perspective.
- Passion for ensuring the inclusion of marginalised communities in AI technologies.
- Previous experience with working in a multi-disciplinary team with diverse skill sets.
Responsibilities
- Undertake participatory research with users to guide the development of research on data ecosystems for large models.
- Have strong literature review skills
- Collaborate with a diverse and multi-disciplinary team.
- Clearly communicate research ideas and results in writing, such as research papers, presentations, or research notes for internal and external audiences.