Unclassified - Palo Alto, CA

posted 3 months ago

Full-time
Palo Alto, CA

About the position

Nexusflow.ai is at the forefront of developing modern enterprise copilots and agents, focusing on last-mile quality, enterprise-grade robustness, and scalable operational costs. Our mission is to empower enterprises to take ownership of their workflow copilots and agents stack, leveraging powerful yet cost-effective compact large language models (LLMs). We specialize in training large language models and creating high-quality development tooling for copilots and agents tailored to specific workflows. Our team has successfully developed the open-source LLM, NexusRaven, which rivals GPT-4 in function calling while being 100 times smaller in model size. Additionally, our team members have contributed to Starling, the top-ranked compact 7B chat model based on human evaluation in the Chatbot Arena. As an Applied Machine Learning Engineer at Nexusflow, you will play a crucial role in powering our LLMs and enhancing our methodologies for achieving last-mile quality tooling for copilots and agents. Your responsibilities will include developing LLMs specifically designed to support copilots and agents tailored for enterprise workflows. You will also be tasked with creating tooling that ensures last-mile quality and robustness for copilot and agent applications, particularly in scenarios where there is a low volume of manually curated data. Furthermore, you will be involved in building application solutions for high-value customer verticals, collaborating closely with the entire team to drive product development, deployment, and customer success.

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
  • Build copilot & agent application solutions for high value customer verticals
  • Collaborate with the whole team for product development, deployment, and customer success

Requirements

  • 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

Nice-to-haves

  • Working experience in fast-paced teams
  • In-depth experience in using or contributing to modern compute frameworks for LLMs (e.g. Deepspeed, Huggingface TGI, FSDP)
  • Experience in turning applied research results into product components
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