Senior Machine Learning Engineer - Content and Contributor Intelligence (Remote - CAN)

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 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 hybrid and does not require you to be located in any particular area in Canada. We welcome applicants from throughout Canada. 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.
• 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
• Productionize and automate model pipelines within Python services.
• Drive and advocate adoption of best practices in ML development and operations, and mentor newer engineers in those practices.

What it takes to succeed:

• Experience developing and productionizing machine learning models, particularly in neural networks, computer vision and LLMs including their supported data pipelines.
• Experience with machine learning using packages such as PyTorch, TensorFlow, Spark MLlib, XGBoost, and Sklearn.
• Strong coding skills in Python or equivalent (Java, 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.
• We highly value experience of working with LLMs, utilizing LLM APIs (OpenAI, Bedrock, etc), prompt engineering and evaluation.

What you'll get:

• There are a variety of factors that go into determining a compensation range, including but not limited to external market benchmark data and years of experience. Based on the anticipated level of experience that we are seeking, we expect the compensation range for this role to be between $[135,000] and $[185,000]. The actual compensation offered may be influenced by a variety of factors, including the candidate’s experience and skill set.

• There may be flexibility with the range included in this posting should a candidate be leveled higher or lower than the posted range.

• This opportunity has the option to be hybrid in all locations across Canada.
• This role is posted to fill an existing position.
• You can find more information about Yelp's five star benefits here!

Closing

At Yelp, we believe that diversity is an expression of all the unique characteristics that make us human: race, age, sexual orientation, gender identity, religion, disability, and education — and those are just a few. We recognize that diverse backgrounds and perspectives strengthen our teams and our product. The foundation of our diversity efforts are closely tied to our core values, which include “Playing Well With Others” and “Authenticity.”

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.

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• 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
• Productionize and automate model pipelines within Python services.
• Drive and advocate adoption of best practices in ML development and operations, and mentor newer engineers in those practices.

• Experience developing and productionizing machine learning models, particularly in neural networks, computer vision and LLMs including their supported data pipelines.
• Experience with machine learning using packages such as PyTorch, TensorFlow, Spark MLlib, XGBoost, and Sklearn.
• Strong coding skills in Python or equivalent (Java, 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.
• We highly value experience of working with LLMs, utilizing LLM APIs (OpenAI, Bedrock, etc), prompt engineering and evaluation.