Machine Learning Research Scientist, Post-Training
Machine Learning Research Scientist, Post-Training
As a Machine Learning Research Scientist, Post-Training, you will focus on optimizing data curation and evaluation to enhance LLM capabilities. This role involves developing novel post-training techniques, including SFT, RLHF, and reward modeling, for text and multimodal modalities. You will also analyze model behavior, identify weaknesses, and contribute to research publications.
About the role
About the Role
Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities.
In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.
Responsibilities
Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.
Design and experiment new approaches to preference optimization.
Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.
Publish research findings in top-tier AI conferences.
Requirements
Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.
Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.
Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.
Excellent written and verbal communication skills
Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
LLM post trainingSFTRLHFreward modelingdata curationevalDeep LearningReinforcement Learninglarge scale model fine tuningpreference modelinginstruction tuning