#LLM-DFL
Our client, a software company specializing in deploying large language models (LLMs) for enterprises and refining these models using sensitive data, recently disclosed raising $15.1 million in a Series A funding round. This funding was co-led by investment firms Canapi Ventures and Nexus Venture Partners. Additionally, Formus Capital and Soma Capital participated in this funding round, contributing to a total raised amount of $19.3 million for our client.
Responsibilities
Own an LLM vertical with a specific domain and optimization focus (e.g. personalization, privacy, efficiency, explainability, or fairness).
Collaborate with our engineering team to deliver real-world applications of your algorithms for our customers.
Co-author papers, patents, and presentations with our research team by integrating other members’ work with your vertical.
Expectations
Although our main products revolve around federated, distributed, and privacy-centric learning, we don’t expect you to have extensive FL (federated learning) experience. We do expect:
Deep domain knowledge in a specific LLM technique / area of research.
Extensive experience in implementing multiple different types of LLM models and architectures in the real world. Comfortability with leading end-to-end projects.
Adaptability and flexibility. In both the academic and startup world, a new finding in the community may necessitate an abrupt shift in focus. You must be able to learn, implement, and extend state-of-the-art research.