Gitty

Lead an LLM vertical focusing on domain-specific optimization, collaborate with engineering to deliver applications, and co-author research outputs. | Deep expertise in LLM techniques, extensive real-world LLM implementation experience, ability to lead projects, and adaptability to new research findings. | #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.

Lead an LLM vertical focused on domain-specific optimization, collaborate with engineering to deploy models, and co-author research outputs. | Deep domain expertise in LLM research and implementation, ability to lead end-to-end projects, adaptability to fast-changing research priorities, and collaboration with engineering and research teams. | #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.