What are the responsibilities and job description for the Applied ML Engineer position at Nexusflow.ai Inc.?
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 of Starling, the #1 ranked compact 7B chat model based on human evaluation in Chatbot 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
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Develop LLMs targeted at powering copilots and agents built for enterprise workflows
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Develop toolings to attain last-mile quality and robustness for copilot & agents applications (especially under low volume of manually curated data)
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Building copilot & agent application solutions for high value customer verticals
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Wear many hats and collaborate with the whole team for product development, deployment and customer success
Qualification
Required
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Research or industrial engineering experience in at least one of the following aspects in the context of large language model or multi-modality models:
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Data curation
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Pre training
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Instruction tuning
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Copilots & agents building
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Capability study and benchmarking
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Excitement to contribute to both applied research and software engineering on productionizing the applied research outcome
Preferred
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Working experience in fast-pace teams
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In-depth experience in using or contributing to modern compute frameworks for LLMs (e.g. Deepspeed, Huggingface TGI)
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Experience in turning applied research results into product components
