What are the responsibilities and job description for the Applied Machine Learning Engineer position at VALID8 Financial?
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.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 : Pre trainingCopilots & agents buildingCapability study and benchmarkingExcitement 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.#J-18808-Ljbffr
