Staff Machine Learning Engineer - Content and Contributor Intelligence (Remote - United States)

Yelp engineering culture is driven by our values : we’re a cooperative team that values individual authenticity and encourages creative solutions to problems.

All new engineers deploy working code their first week, and we strive to broaden individual impact with support from managers, mentors, and teams.

At the end of the day, we’re all about helping our users, growing as engineers, and having fun in a collaborative environment.

Yelp’s mission of connecting people with great local businesses requires the use of cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) to scale across a vast and diverse base of users and businesses spanning various geographical locations.

As a Staff-level ML Engineer on the Content Contributor Intelligence team, you will help build connections across millions of users and business listings.

Your work will involve using cutting-edge industry tools, including neural networks (NNs), large language models (LLMs), and various embedding techniques for text, images, and videos.

Additionally, you will apply traditional ML methods such as XGBoost and linear models to enhance our systems.

You’ll be responsible for turning raw data into valuable signals and building ML systems end-to-end.

This includes the full ML lifecycle from training models to deploying them in production, as well as contributing to the ML platforms these models rely on.

This opportunity is fully remote and does not require you to be located in any particular state within the US.

We welcome applicants from throughout the US.

We’d love to have you apply, even if you don’t feel you meet every single requirement in this posting.

At Yelp, we’re looking for great people, not just those who simply check off all the boxes.

• Conduct end-to-end analyses, wrangling data via SQL or Python, to statistical modeling, to hypothesizing and presenting business ideas.
• Mentor and guide junior engineers, fostering a culture of learning and technical excellence.
• Work with large and complex textual and visual datasets.
• Support the development and deployment of projects involving machine learned models for offline, batch-based data products as well as models deployed to online, real-time services.
• Work in the contributor and visual intelligence team on text and visual understanding, along with fine tuning transformer models to derive embeddings for multiple input types