About Nexusflow.ai
Check all associated application documentation thoroughly before clicking on the apply button at the bottom of this description.
Modern enterprise copilots & agents call for last-mile quality, enterprise-grade robustness and scalable operation costs, beyond simplified programming interfaces for generative AI.
Nexusflow tackles this challenge, enabling enterprises to own their workflow copilots & agents stacked on top of powerful yet cost-effective, compact LLMs.
We train large language models and build last-mile quality dev tooling for copilots & agents on your enterprise workflows.
Our team has built the open-source LLM, NexusRaven-V2, rivaling GPT-4 in function calling with a 100X smaller model size.
Our team members are also behind the scenes of Starling, the #1 ranked compact 7B chat model based on human evaluation in Chatbot Arena.
Position
Nexusflow is currently adding Backend Engineers to our team. Our Backend Engineers package up our technology in models and last-mile quality tooling.
They will be the driving force to build our products and solutions, in extensive collaboration with our ML Engineers and Front-end Engineers.
Responsibilities
- API system development for copilot & agent quality tooling
- API system development for copilot serving and integration with a focus on enterprise-grade requirements in the following areas :
- Integration with on-prem & cloud compute vendors
- Integration with software tools required in customer-oriented solutions
Distributed system and optionally GPU performance optimizationWear many hats and collaborate with the whole team for product development, deployment, and customer success
Qualification
Required
- Experience in ML model or ML data pipeline deployment (on-prem or on cloud)
- Experience in building backend for application or platform API systems
Preferred
- Working experience in a fast-paced team environment
- Experience in using or contributing to modern compute frameworks for LLMs (e.g. Deepspeed, Huggingface TGI, FSDP)
- Experience in projects involving LLMs
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