United States
HEARTLAND BUSINESS SYSTEMS LLC
Nexusflow.ai Inc.
Applied ML Engineer
United States
Palo Alto, California, United States
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 huggingface.co/Nexusflow/NexusRaven-V2-13B, rivaling GPT-4 in function calling with a 100X smaller model size. Our team members are also behind the scenes of Starling huggingface.co/berkeley-nest/Starling-LM-7B-alpha, the #1 ranked compact 7B chat model based on human evaluation in Chatbot Arena huggingface.co/spaces/lmsys/chatbot-arena-leaderboard.
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
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