Research Engineer, Knowledge Bases
3 weeks ago
We are looking for Research Engineers to help us redesign how Language Models interact with external data sources. Many of the paradigms for how data and knowledge bases are organized assume human consumers and constraints. This is no longer true in a world of Language Models Your job will be to design new architectures for how information is organized, and train language models to optimally use those architectures.
Responsibilities:
Designing and implementing from scratch new information architecture strategies
Performing finetuning and reinforcement learning to “teach” language models how to interact with new information architectures
Building “hard” knowledge base eval sets to help identify failure modes of how language models work with external data
Extending traditional ideas like RAG into heterogeneous data types (image, tables, relational data, etc.)
You may be a good fit if you:
Have significant Python programming experience
Have good machine learning research experience
Have experience developing software that utilizes Large Language Models such as Claude
Are results-oriented, with a bias towards flexibility and impact
Pick up slack, even if it goes outside your job description
Enjoy pair programming (we love to pair)
Want to partner with world-class ML researchers to develop new LLM capabilities
Care about the societal impacts of your work
Have clear written and verbal communication
Strong candidates will also have experience with:
Developing scalable distributed information retrieval systems, such as search engines, knowledge graphs, indexing, ranking, query understanding, and distributed data processing
Conducting research to advance search quality and knowledge base systems
Understanding Retrieval Augmented Generation (RAG) and its limitations
Collaborating with product teams to quickly prototype and deliver innovative solutions
Deadline to apply: None. Applications will be reviewed on a rolling basis.
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