Machine Learning Research Scientist, Post-Training
Scale AI · San Francisco, CA; Seattle, WA; New York, NY · San Francisco, CA
CHECKED 13H AGO
Infrastructureonsite$166k–$207k
At a glance
Highlights
- Develop novel post-training techniques to boost LLM text and multimodal capabilities.
- Partner with top foundation labs to shape next-gen generative AI models.
- Publish findings in top-tier AI conferences while improving model alignment.
What they offer
- Equity compensation
- Comprehensive health, dental and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous PTO
- Commuter stipend
Ideal candidate
A research-driven ML scientist with deep curiosity for LLM alignment and generalization who thrives collaborating with top labs and translating data-driven insights into impactful models.
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 areas of machine learning at major conferences and/or journals
- Previous experience in a customer facing role
Lab profile
About Scale AI
AI data foundry providing high-quality data for training, fine-tuning, and evaluating AI systems.
What they build
- Training, fine-tuning, and evaluation data for frontier models
- Multimodal and coding data for audio, image, video and world models
- Custom AI applications for enterprise and global public sector
Known for
- Leading data partner for frontier model developers
- Trusted across enterprise, defense and public sector
- Depth in multimodal, coding and physical AI
- End-to-end capability from strategy to delivery and evaluation
Key facts
- Founded in 2016
- Headquartered in San Francisco, California
- Private company backed by Accel, Founders Fund, Index Ventures, Nvidia and Meta