How does Malt leverage Artificial Intelligence and data analytics to match freelance talent with the right opportunities more effectively ?

Malt has been using AI since 2019 to power our recommendation system , finding the best freelancers for a given project among more than 800,000 profiles on the platform . Recently , we ’ ve built new features powered by language models to help any company describe their need with a few words , long paragraphs , or even a PDF of a job description and translate that into a talent search , identifying the right competencies for the job , extracting the relevant information and generating the description for the freelancer .

It not only helps clients save time , but it has also increased the attractiveness of projects for clients assisted with AI , who are more likely to get positive responses from highly sought-after talents and start their projects faster .

Malt employs state-of-the-art AI models to enhance talent matching , moving beyond simple keyword searches to deep learning-powered recommendations . By utilising large language models , Malt ensures that freelancers with relevant skills – even if they don ’ t use exact matching terms – are surfaced in search results .

We are building our own small language models powered by the millions of projects and profiles we ’ ve assessed over the last decade . This enables us to have models that are more performant and keep a low carbon footprint for our solution .

Language models are very powerful for finding the right talent since they remove the burden of typing the exact keyword by having a sense of semantic similarity . For instance , before , a client looking for a ‘ data scientist ’ may miss ‘ Machine Learning ’ profiles ; now they only need to say , “ I need someone to build an internal AI assistant for my company .”

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