Role overview

Scale AI is seeking a Machine Learning Research Scientist to advance post-training techniques for large language models. You will develop novel methods to improve how generative models are aligned and refined, working with leading AI labs to enhance both text and multimodal capabilities.

Key responsibilities

Research and develop post-training techniques including supervised fine-tuning, reinforcement learning from human feedback, and reward modeling to strengthen core LLM capabilities.
Design and experiment with new approaches to preference optimization for generative models.
Analyze model behavior to identify weaknesses and propose solutions for reducing bias and improving robustness.
Publish research findings in top-tier artificial intelligence conferences.
Collaborate with researchers and engineers to establish best practices in data-driven AI development.
Partner with foundation model labs to provide technical and strategic guidance on next-generation generative AI systems.

Requirements

Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related discipline.
Deep expertise in deep learning, reinforcement learning, and large-scale model fine-tuning.
Hands-on experience with post-training methods such as reinforcement learning from human feedback, preference modeling, or instruction tuning.
Strong written and verbal communication skills.
Published research at major conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or CVPR is preferred.
Previous experience in customer-facing roles is a plus.

What makes this role worth considering

This position offers the opportunity to shape the future of generative AI by working directly with industry-leading research teams. You will be based in San Francisco, Seattle, or New York, collaborating with top foundation model labs to define how the next generation of AI systems are developed and improved.