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Senior Machine Learning Engineer (Trust and Safety) (Remote – UK)



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.

Are you intrigued by data? Yelp has hundreds of millions of pieces of user-contributed content, millions of users and business listings, and hundreds of thousands of advertising customers – and all of these numbers are constantly growing. Making sense of this data, deducing relationships between variables, and figuring out different interactions is hard work, but these insights are hugely impactful to Yelp’s business.

The Trust & Safety team at Yelp focuses on surfacing high-quality content that reflects authentic experiences generated by real Yelpers who are engaged with the community. We invest heavily in ML models and infrastructure to detect and understand content spam on our platform. As an engineer on our team, you’d be helping our users, every day, to make trustworthy connections with great local businesses, by deciding where to eat, which mover to call, or best places to visit in a new city. 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.

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:

Be responsible for turning raw data into valuable signals and building the ML system end-to-end.

Get involved with the full ML lifecycle from training models to deploying them in production.

Conduct analyses, wrangling data via SQL or Python, to statistical modeling, to hypothesizing and presenting business ideas.

Lead the development and deployment of reliable, scalable, and maintainable systems.

Productionize and automate model pipelines within Python services.

Provide technical mentorship and coaching to the team members.

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.

Expertise in Python (including pandas, NumPy, scikit-learn, Tensorflow, Keras, XGBoost).

Experience in Spark, cloud (AWS), and microservices (Docker, Kubernetes).

Ability to work independently and autonomously in a fast-paced environment.

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.



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