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Salary: USD 180,600 - 225,750 / annual
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 alignment and generalization of generative models, working closely with leading AI labs and foundation model teams.
Key responsibilities include:
- Research and develop novel post-training techniques including SFT (Supervised Fine-Tuning), RLHF (Reinforcement Learning from Human Feedback), and reward modeling to enhance LLM capabilities in text and multimodal modalities
- Design and experiment with new approaches to preference optimization
- Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness
- Collaborate with researchers and engineers to define best practices in data-driven AI development
- Partner with top foundation model labs to provide technical and strategic input on next-generation generative AI models
- Publish research findings in top-tier AI conferences
Required qualifications:
- Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or 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
- Published research in machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) or journals
- Excellent written and verbal communication skills
- Previous experience in customer-facing roles preferred
Scale AI works with industry-leading AI labs to provide high-quality data and accelerate progress in generative AI research. The company partners with organizations like Meta, Mayo Clinic, and U.S. government agencies.
