
Machine Learning Research Scientist / Research Engineer, Post-Training
- USA
- Permanent
- Full-time
- 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.
- 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
- Previous experience in a customer facing role.