Applied ML Engineer
23 hours ago
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. 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.
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).
- Build copilot & agent application solutions for high-value customer verticals.
- Collaborate with the whole team for product development, deployment, and customer success.
- Research or industrial engineering experience in at least one of the following aspects in the context of large language model or multi-modality models:
- Pre-training.
- Copilots & agents building.
- Capability study and benchmarking.
- Excitement to contribute to both applied research and software engineering on productionizing the applied research outcome.
- Working experience in fast-paced 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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