Machine Learning Research Scientist / Research Engineer, Post-Training

3 Months ago • All levels • Research Development • $220,000 PA - $325,000 PA

Job Summary

Job Description

Scale collaborates with leading AI labs to provide high-quality data, accelerating advancements in GenAI research. The role focuses on optimizing data curation and evaluation to enhance LLM capabilities across text and multimodal modalities. The responsibilities include researching and developing novel post-training techniques like SFT, RLHF, and reward modeling, designing new approaches to preference optimization, and analyzing model behavior to identify weaknesses and propose solutions for bias mitigation and model robustness. This role involves collaboration with researchers and engineers to establish best practices in data-driven AI development and providing technical and strategic input to top foundation model labs for the development of next-generation generative AI models.
Must have:
  • Ph.D. or Master's in CS, Machine Learning, AI, or related fields.
  • Deep understanding of deep learning, reinforcement learning, and model fine-tuning.
  • Experience with post-training techniques like RLHF or instruction tuning.
  • Excellent written and verbal communication skills.
Good to have:
  • Published research at major AI conferences or journals.
  • Previous experience in a customer-facing role.

Job Details

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities.

In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.

You will:

  • 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.

Ideally you’d have:

  • 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.

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