Machine Learning Research Scientist, Post-Training
$166k - $207k • Remote • San Francisco, CA; Seattle, WA; New York, NY
Posted 7d ago
Job Location
San Francisco, CA; Seattle, WA; New York, NY
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Research Engineer
About the job
Scale works with leading AI labs to accelerate GenAI research by providing high-quality data. This role focuses on optimizing data curation and evaluation to enhance LLM capabilities in text and multimodal modalities. You will develop novel methods to improve the alignment and generalization of large-scale generative models, collaborate with researchers and engineers on best practices in data-driven AI development, and partner with foundation model labs to provide technical and strategic input on next-generation generative AI models.
Responsibilities
- Research and develop novel post-training techniques (SFT, RLHF, 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.
- Publish research findings in top-tier AI conferences.
Requirements
- 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 machine learning at major conferences or journals.
- Previous experience in a customer-facing role.
Benefits
- Base salary
- Equity
- Comprehensive health, dental and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous PTO
- Commuter stipend