Applied ML Engineer

About Nexusflow.ai

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 ofStarling, the #1 ranked compact 7B chat model based on human evaluation inChatbot Arena.

Position: Applied ML Engineers

Nexusflow is currently adding Applied ML Engineers to our team.Our Applied ML Engineers power our LLMs as well as Nexusflow’s methodologies for last-mile quality tooling for copilots and agents. They build the base layer of Nexusflow’s stack, contributing to tooling product and customer solutions.

Responsibilities

• Develop LLMs targeted at powering copilots and agents built for enterprise workflows

• Develop toolings to attain last-mile quality and robustness for copilot & agents applications (especially under low volume of manually curated data)

• Building copilot & agent application solutions for high value customer verticals

• Wear many hats and collaborate with the whole team for product development, deployment and customer success

Qualification

Required

• Research or industrial engineering experience in at least one of the following aspects in the context of large language model or multi-modality models: 

  • Data curation

  • Pre training

  • Instruction tuning

  • Copilots & agents building

  • Capability study and benchmarking

• Excitement to contribute to both applied research and software engineering on productionizing the applied research outcome

Preferred

• Working experience in fast-pace teams

• In-depth experience in using or contributing to modern compute frameworks for LLMs (e.g. Deepspeed, Huggingface TGI) 

• Experience in turning applied research results into product components