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The company is looking to optimize data curation and algorithmic improvements to enhance large language models (LLMs) capabilities in core areas such as instruction following, factuality, coding, multilingual and multimodal understanding.

Requirements

• 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.
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals

Responsibilities

• Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in areas of instruction following, factuality, coding, multilingual and multimodal understanding.
• 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.

Other

• Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.
• Excellent written and verbal communication skills
• Previous experience in a customer facing role.
• Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO.