Machine Learning Research Scientist, Evaluations

Roles and Responsibilities:

• 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.

Ideally you’d have:

• 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, 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.