Staff Machine Learning Engineer, Fakespot

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Why Mozilla?

Mozilla Corporation is the non-profit-backed technology company that has shaped the internet for the better over the last 25 years. We make pioneering brands like Firefox, the privacy-minded web browser, and Pocket, a service for keeping up with the best content online. Now, with more than 225 million people around the world using our products each month, we’re shaping the next 25 years of technology and helping to reclaim an internet built for people, not companies. Our work focuses on diverse areas including AI, social media, security and more. And we’re doing this while never losing our focus on our core mission – to make the internet better for people. 

The Mozilla Corporation is wholly owned by the non-profit 501(c) Mozilla Foundation. This means we aren’t beholden to any shareholders — only to our mission. Along with thousands of volunteer contributors and collaborators all over the world, Mozillians design, build and distribute open-source software that enables people to enjoy the internet on their terms. 

About this team and role:

Fakespot is now part of Mozilla, where our mission to bring trust and transparency back to the eCommerce space is now aligned with ensuring the Internet is a global resource, open and accessible to all. Fakespot leverages machine learning, and other state-of-the-art technologies to detect spurious product reviews and vendors so consumers can make informed purchasing decisions based on real feedback by real people. Fakespot also facilitates the shoppers’ decision making process by utilizing language models to interactively summarize and highlight product features.  

At the Fakespot team, you will have the opportunity to play a crucial role in bringing trustworthy tools to millions of Firefox and Fakespot users. By joining our team, you will be at the forefront of ethical and responsible AI, and contribute to a trustworthy Internet. 

What you’ll do:

• Apply statistical and machine learning techniques to process and analyze unstructured textual data
• Develop and finetune machine learning models for tasks such as entity recognition, classification, and text generation
• Utilize pretrained language models (e.g. GPT, LLAMA) and adapt them for specific use cases
• Optimize the models for production usage, including considerations for scalability, latency, …