LLM Post-Training Research Scientist

Scale AI, Inc.; Seattle (WA); On-site; USD 181,000 - 226,000; Full time

Benefits offered by this job

Health, dental and vision coverage

Retirement benefits

Learning and development stipend

Generous PTO

Commuter stipend

Job summary

Scale AI, Inc. is seeking Research Scientists and Research Engineers to advance LLM post-training techniques for text and multimodal data.

You will develop methods to align and generalize large-scale generative models and collaborate with researchers to define data-driven best practices in data curation and evaluation. This role involves publishing results at top AI conferences, addressing bias and robustness, and working with industry labs to shape the next generation of generative models.

Qualifications

• 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.

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.

Skills

LLM post-training

Data curation

Eval for LLMs

Communication skills

Education

PhD or Master's in CS/ML/AI

Tools

Python

PyTorch

Job description

Scale AI, Inc. is seeking Research Scientists and Research Engineers to advance LLM post-training techniques for text and multimodal data.

You will develop methods to align and generalize large-scale generative models and collaborate with researchers to define data-driven best practices in data curation and evaluation. This role involves publishing results at top AI conferences, addressing bias and robustness, and working with industry labs to shape the next generation of generative models.