Research Scientist I
2 weeks ago
Develop novel preference learning models that account for heterogeneous populations, ensuring fairness and inclusivity in LLM alignment.
Design and implement resource-efficient alignment techniques, including inference-time methods and parameter-efficient fine-tuning.
Construct and analyze Singapore-centric alignment datasets, capturing cultural, racial, religious, and linguistic diversity.
Develop evaluation benchmarks to assess LLM alignment quality across diverse sub-groups.
Collaborate with interdisciplinary teams to integrate domain knowledge from linguistics, social sciences, and computational AI.
Publish research findings in top-tier conferences and journals.
ABOUT A*STAR AND CFAR
At A*STAR, we make it our mission to attract and develop a diversity of talent along the research, innovation, and enterprise value chain, with career paths developed for scientists, engineers, and entrepreneurs. In return, we commit to investing in the personal and professional growth of each of our officers.
CFAR has a team of exemplary AI researchers committed to solve most exciting problems in the future AI, including IEEE fellows, chairs, scholars, and advisors among some of the most highly cited researchers in the world. Our AI Centre provides a unique research environment for fundamental AI research and cultivate next-gen AI scientists. If you are passionate about advancing AI alignment in multicultural societies and enabling scalable, efficient LLM adaptation, we encourage you to apply.
JOB REQUIREMENT
PhD in Computer Science, Computational Mathematics, Machine Learning, or related fields.
Strong background in AI, deep learning, and preference learning, with expertise in areas such as unsupervised learning, generative models, or controlled text generation.
Solid foundation in mathematics, capable of independent theoretical proofs, algorithm design, and integration with machine learning, optimization, and statistical modeling.
Experience with LLM alignment techniques, such as Reinforcement Learning with Human Feedback (RLHF) or Direct Preference Optimization (DPO).
Proficiency in developing efficient AI models, including parameter-efficient fine-tuning or inference-time adaptation methods.
Strong publication records in prestigious conferences and journals, such as NeurIPS, ICML, ICLR, AAAI, IJCAI, JMLR, TPAMI, AI, TNNLS, TKDE, TCYB.
Ability to work independently and collaboratively within interdisciplinary research teams.
**Type of Employment**: Full-Time
**Minimum Experience**: 1 Year
**Work Location**: Immunos
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