Job Information
Microsoft Corporation Cambridge Residency Programme: ML Post-Doc in Equitable AI in Cambridge, United Kingdom
Cambridge Residency Programme: 2-year ML Post-Doc in Equitable AI
The Teachable AI Experiences (Tai X) team at Microsoft Research Cambridge (UK) is a multidisciplinary research group that brings together different skill sets to innovate human-AI interaction. The team is hiring a machine learning (ML) researcher to push the state-of-the-art of generative AI systems, with a goal of ensuring AI experiences work for marginalised communities. The candidate should have deep technical knowledge of current generative AI models, with a particular focus on multi-modal models (e.g. image-text models like GPT-4Vision, LLaVa and CLIP). They should also have demonstrated research experience in at least one of the following areas: fairness/bias, transparency/interpretability, AI ethics, accessibility, data-centric AI, model robustness, out-of-domain generalisation, long-tailed learning, and generative evaluation methods. The candidate should be able to approach technical problems with a multi-disciplinary perspective and be passionate about building equitable and fair AI technologies for all users.
Responsibilities
Drive a socio-technical research agenda that deepens our understanding of how generative AI models work for users in real-world scenarios.
Write research code to develop and validate new approaches, or develop novel theoretical and practical insights.
Work within a multi-disciplinary team of HCI researchers, designers and engineers, bringing machine learning knowledge and insights to inform the team's core projects.
Deliver high-quality research outputs in the form of publications in top-tier machine learning conferences and journals.
Communicate research ideas and results in writing, such as research papers, presentations, or research notes for internal and external audiences.
Influence Microsoft products, services and policy through deep research insights.
Qualifications
Required Qualifications:
A PhD in Computer Science, Machine Learning, AI, or a related field (candidates who are expected to complete their PhD before the end of 2024 will also be considered).
Strong technical understanding of state-of-the-art generative AI models, with a particular focus of multi-modal models.
Demonstrable research expertise in any of the following fields: fairness/bias, transparency/interpretability, AI ethics, accessibility, data-centric AI, model robustness, out-of-domain generalisation, long-tailed learning, evaluation methods (e.g. through peer-reviewed publications in these topics at top-tier venues).
Strong publication record in top-tier machine learning conferences and journals (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV/ECCV, FAccT, EMNLP, ACL).
Strong proficiency with PyTorch.
Effective communication skills and ability to work in a collaborative environment.
Preferred 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 working in a multi-disciplinary team with diverse skill sets.
Contribution to open-source code projects (e.g. on GitHub).
Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations (https://careers.microsoft.com/v2/global/en/accessibility.html) .
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