Qualifications
Your responsibilities will include:
Researching and developing cutting-edge post-training methodologies such as SFT, RLHF, and reward modeling to amplify LLM capabilities in text and multimodal contexts.
Designing and experimenting with innovative approaches to optimize preferences.
Analyzing model behaviors, identifying weaknesses, and proposing solutions for bias mitigation and enhancing model robustness.
Publishing your research findings in premier AI conferences.
Preferred qualifications:
Ph. D. or Master’s degree in Computer Science, Machine Learning, AI, or a related discipline.
In-depth knowledge of deep learning, reinforcement learning, and large-scale model fine-tuning.
Experience with post-training strategies like RLHF, preference modeling, or instruction tuning.
Exceptional written and verbal communication skills.
Published work in machine learning at notable conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals.
Prior experience in a customer-facing role.
About the job
At Scale AI, we collaborate with leading AI laboratories to supply high-quality data and foster advancements in Generative AI research. We seek innovative Research Scientists and Research Engineers with a strong focus on post-training techniques for Large Language Models (LLMs), including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and reward modeling. This position emphasizes optimizing data curation and evaluation processes to boost LLM performance across text and multimodal formats.
In this pivotal role, you will pioneer new methods to enhance the alignment and generalization of extensive generative models. You will work closely with fellow researchers and engineers to establish best practices in data-driven AI development. Additionally, you will collaborate with top foundation model labs, providing critical technical and strategic insights for the evolution of next-generation generative AI models.
