Full-Time
Develops advanced AI language models
No salary listed
Senior
Remote in USA
Flexible working hours and remote options available.
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Hugging Face develops machine learning models focused on understanding and generating human-like text. Their main products include advanced natural language processing (NLP) models like GPT-2 and XLNet, which can perform tasks such as text completion, translation, and summarization. Users can access these models through a web application and a repository, making it easy for researchers, developers, and businesses to integrate AI into their applications. Unlike many competitors, Hugging Face offers a freemium model, providing basic features for free while charging for advanced functionalities and enterprise solutions tailored to large organizations. The company's goal is to empower clients to utilize machine learning for various text-related tasks, enhancing their applications with sophisticated language capabilities.
Company Size
501-1,000
Company Stage
Series D
Total Funding
$395.7M
Headquarters
New York City, New York
Founded
2016
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Flexible Work Environment
Health Insurance
Unlimited PTO
Equity
Growth, Training, & Conferences
Generous Parental Leave
Artificial intelligence (AI) is now a household word, thanks to the popularity of large language models like ChatGPT. These large models are trained on the whole internet and often have hundreds of billions of parameters — settings inside the model that help it guess what word comes next in a sequence. The more parameters, the [] The post AI Explained: What’s a Small Language Model and How Can Business Use It? appeared first on PYMNTS.com.
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. The entire AI landscape shifted back in January 2025 after a then little-known Chinese AI startup DeepSeek (a subsidiary of the Hong Kong-based quantitative analysis firm High-Flyer Capital Management) launched its powerful open source language reasoning model DeepSeek R1 publicly to the world, besting U.S. giants such as Meta. As DeepSeek usage spread rapidly among researchers and enterprises, Meta was reportedly sent into panic mode upon learning that this new R1 model had been trained for a fraction of the cost of many other leading models yet outclassed them for as little as several million dollars — what it pays some of its own AI team leaders.Meta’s whole generative AI strategy had until that point been predicated on releasing best-in-class open source models under its brand name “Llama” for researchers and companies to build upon freely (at least, if they had fewer than 700 million monthly users, at which point they are supposed to contact Meta for special paid licensing terms). Yet DeepSeek R1’s astonishingly good performance on a far smaller budget had allegedly shaken the company leadership and forced some kind of reckoning, with the last version of Llama, 3.3, having been released just a month prior in December 2024 yet already looking outdated.Now we know the fruits of that reckoning: today, Meta founder and CEO Mark Zuckerberg took to his Instagram account to announced a new Llama 4 series of models, with two of them — the 400-billion parameter Llama 4 Maverick and 109-billion parameter Llama 4 Scout — available today for developers to download and begin using or fine-tuning now on llama.com and AI code sharing community Hugging Face.A massive 2-trillion parameter Llama 4 Behemoth is also being previewed today, though Meta’s blog post on the releases said it was still being trained, and gave no indication of when it might be released
5 years ago, Hugging Face launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.
Model repository Hugging Face launched Yourbench, an open-source tool where developers and enterprises can create their own benchmarks to test model performance against their internal data.
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Every AI model release inevitably includes charts touting how it outperformed its competitors in this benchmark test or that evaluation matrix. However, these benchmarks often test for general capabilities. For organizations that want to use models and large language model-based agents, it’s harder to evaluate how well the agent or the model actually understands their specific needs. Model repository Hugging Face launched Yourbench, an open-source tool where developers and enterprises can create their own benchmarks to test model performance against their internal data. Sumuk Shashidhar, part of the evaluations research team at Hugging Face, announced Yourbench on X. The feature offers “custom benchmarking and synthetic data generation from ANY of your documents. It’s a big step towards improving how model evaluations work.”He added that Hugging Face knows “that for many use cases what really matters is how well a model performs your specific task