Senior ML Engineer - Content and Contributor Intelligence (Remote - United Kingdom)

Summary 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 an ML engineer, you will have the opportunity to foster these connections across millions of users and business listings using cutting-edge industry tools such as neural networks (NNs), large language models (LLMs), and traditional ML methods like XGBoost or linear models. You’ll be responsible for turning raw data into valuable signals and building the ML system 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 or MLOps. This opportunity requires you to be located in the United Kingdom. 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. What you'll do: Conduct end-to-end analyses, wrangling data via SQL or Python, to statistical modeling, to hypothesizing and presenting business ideas. Work with large and complex 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 content and contributor intelligence team on text and visual understanding, along with fine tuning transformer models to derive embeddings for multiple input types Productionize and automate model pipelines within Python services. Drive and advocate adoption of best practices in MLOps (Machine Learning Operations). What it takes to succeed: Experience developing and productionizing machine learning models, including their supported data pipeline. Experience with machine learning using packages such as TensorFlow, PyTorch, Spark MLlib, XGBoost, Sklearn, etc. Strong coding skills in Python or equivalent (Python, Java and C++). Solid understanding of engineering and infrastructure best practices. The curiosity to uncover promising solutions to new problems, and the persistence to carry your ideas through to an end goal. What you'll get: Full responsibility for projects from day one, a collaborative team, and a dynamic work environment. Competitive salary, a pension scheme, and an optional employee stock purchase plan. 25 days paid holiday (rising to 29 with service), plus one floating holiday. £150 monthly reimbursement to help cover remote working expenses. £81 caregiver reimbursement to support dependent care for families. Private health insurance, including dental and vision. Flexible working hours and meeting-free Wednesdays. Regular 3-day Hackathons, bi-weekly learning groups, and productivity spending to support and encourage your career growth. Opportunities to participate in digital events and conferences. £81 per month to use toward qualifying wellness expenses. Quarterly team offsites. Closing Yelp values diversity. We’re proud to be an equal opportunity employer and consider qualified applicants without regard to race, color, religion, sex, national origin, ancestry, age, genetic information, sexual orientation, gender identity, marital or family status, veteran status, medical condition, disability, or any other protected status. Note: Yelp does not accept agency resumes. Please do not forward resumes to any recruiting alias or employee. Yelp is not responsible for any fees related to unsolicited resumes. #LI-Remote Recruiting and Applicant Privacy Notice


Conduct end-to-end analyses, wrangling data via SQL or Python, to statistical modeling, to hypothesizing and presenting business ideas. Work with large and complex 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 content and contributor intelligence team on text and visual understanding, along with fine tuning transformer models to derive embeddings for multiple input types Productionize and automate model pipelines within Python services. Drive and advocate adoption of best practices in MLOps (Machine Learning Operations).


Experience developing and productionizing machine learning models, including their supported data pipeline. Experience with machine learning using packages such as TensorFlow, PyTorch, Spark MLlib, XGBoost, Sklearn, etc. Strong coding skills in Python or equivalent (Python, Java and C++). Solid understanding of engineering and infrastructure best practices. The curiosity to uncover promising solutions to new problems, and the persistence to carry your ideas through to an end goal.