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Salary: USD 180,600 - 225,750 / annual
Scale 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 directly with leading AI labs and foundation model researchers.
Key responsibilities include:
- Research and develop post-training techniques (SFT, RLHF, reward modeling) for text and multimodal LLMs
- Design and experiment with new preference optimization approaches
- Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and robustness
- Publish research findings at top-tier AI conferences
- Collaborate with researchers and engineers to define best practices in data-driven AI development
- Partner with leading foundation model labs to provide technical and strategic input
Ideal candidates will have a Ph.D. or Master's in Computer Science, Machine Learning, AI, or related field, with deep expertise in deep learning, reinforcement learning, and large-scale model fine-tuning. Published research at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR) is highly valued. Experience with post-training techniques and customer-facing roles is a plus.
Scale works with industry leaders including Meta, Ernst & Young, Mayo Clinic, and U.S. government agencies to develop reliable AI systems for critical decisions. The company provides high-quality data and full-stack technologies powering the world's leading models.
