The opportunity
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) and evaluation.
What you'll do
Analyze model behavior to identify, characterize, and diagnose failure modes: in frontier LLMs and Agents. You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA.
Design and build benchmarks and evaluation methods that measure LLM: capabilities in both text and multimodal modalities.
Apply post-training expertise (SFT, RLHF, reward modeling) to connect: observed failures to the data and training interventions that address them.
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.
What they're looking for
- Experience with post-training techniques such as RLHF, preference modeling,: or instruction tuning, and with LLM evaluation or benchmark development.
- 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.
