Cambridge Residency Programme

Overview We are seeking a Machine Learning Researcher candidate for our Cambridge Residency Programme in the area of People-Centric AI at Microsoft Research Cambridge (UK). People-Centric AI is a multi-disciplinary effort to develop knowledge, model capabilities, and experiences that enable human agency and skill, support creativity and collaboration, and ensure equitable representation and participation. Key machine learning challenges that we aim to address include, but are not limited to, human-in-the-loop learning, uncertainty quantification, value alignment, interpretability, fairness and bias mitigation, as well as related areas. We do this by blending research disciplines, design, and engineering. A key aim is to produce principles and insights to shape human-AI interaction, especially as models are given greater flexibility and control over the content and interfaces they generate, and as the practices and expectations of people using these systems evolve. For us, success means delivering AI-enabled experiences that empower all people while preserving and enhancing human capability and connection. We encourage applications from all candidates who are excited to tackle the deep challenges in People-Centric AI, including but not limited to those with backgrounds in deep learning, foundation models, generative AI, computer vision, multi-modal models, deep reinforcement learning, fairness, and efficient, scalable and robust machine learning. Responsibilities The successful candidate will design and implement methods to improve human‑AI collaboration, such as integrating human feedback into model development, applying reinforcement learning, and modelling decision‑making processes. They will contribute to the finetuning of large language models, publish at top research venues, and translate findings into technical and design insights that shape future AI systems. Qualifications Required/Minimum Qualifications:   A PhD in a relevant field (e.g. ML, Computer Science, Mathematics) or equivalent industry experience. Expertise in ML, evidenced by top-tier publications and/or experience. Demonstratable experience in conceptualizing, implementing and evaluating machine learning approaches. Ability to clearly communicate your research to a diverse audience. Motivated to tackle technological problems rooted in real-world human needs, including an interest in how AI systems interact with people and society, and a commitment to designing inclusive, socially impactful technologies. Collaboration skills – the candidate can iterate their technical solutions with feedback from the team/users, rather than seeking to solve problems independently. Preferred/Additional Qualifications: Broad ML education and experiences, covering, for example: statistical methods, probabilistic modelling, RL, deep learning, LLM training, decision-making processes. Experience with multi-modal models and world models. Strong ML coding and engineering skills. Qualifications Publications at top research venues in machine learning, artificial intelligence, natural-language processing, or adjacent areas. Completed or on track to a PhD in ML or have equivalent industrial experience. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. 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 with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.