Preference Model is building the next generation of training data to power the future of AI. Backed by a16z and South Park Commons.
About the role
Models of the future will be able to train themselves on tasks that they are not good at. We are interested in investigating how far we can push the boundaries of self-directed learning. We are looking for machine learning Research Engineers or Research Scientists to push the frontier of post-training on large language models in a role that blends research and engineering, requiring you to implement novel approaches and shape research directions.
What they're looking for
- Experience running end-to-end LLM post-training pipelines of models sizes at least 7B in size
- Proficiency in Python and PyTorch or JAX
- Experience with at least one modern RL training framework
- Experience building and operating ML infrastructure at scale
More about this role
Preference Model is building automated ML research engineering.
Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.
Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.
Models of the future will be able to train themselves on tasks that they are not good at. We are interested in investigating how far we can push the boundaries of self-directed learning. We are looking for machine learning Research Engineers or Research Scientists to push the frontier of post-training on large language models in a role that blends research and engineering, requiring you to implement novel approaches and shape research directions.
Train and evaluate models on our proprietary RL environments to validate data quality, surface gaps in task coverage, and close the feedback loop between environment design and...
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