Senior ML Engineer - Remote Canada, End-to-End Production AI

Yelp; Chicago (IL); Hybrid; USD 135,000 - 185,000; Full time

Benefits offered by this job

Health insurance

Job summary

Yelp is seeking a Staff-level ML Engineer to join the Content Contributor Intelligence team. You will build ML systems end-to-end—from training models to deployment—using neural networks, LLMs, and embeddings for text, images, and videos.

You will mentor engineers and collaborate across teams in a hybrid role that welcomes applicants from multiple locations. You will work with PyTorch, TensorFlow, Spark MLlib, XGBoost and Scikit-learn, contributing to ML platforms and best practices across the

Qualifications

• Experience developing and productionizing machine learning models, including neural networks, computer vision and LLMs.
• Experience with ML packages such as PyTorch, TensorFlow, Spark MLlib, XGBoost, and Sklearn.
• Strong Python or Java/C++ coding skills.
• Solid understanding of engineering and infrastructure best practices.
• Experience with LLMs, APIs (OpenAI, Bedrock, etc), prompt engineering and evaluation.

Responsibilities

• Conduct end-to-end analyses, wrangling data via SQL or Python, to statistical modeling and business ideas.
• Mentor junior engineers and foster a culture of learning and technical excellence.
• Work with large and complex textual and visual datasets.
• Produce ML projects for offline batch and online real-time services.
• Work on text and visual understanding, fine-tuning transformer models for embeddings.
• Productionize and automate model pipelines within Python services.
• Advocate best practices in ML development and operations and mentor others.

Skills

Neural networks

LLMs

Python

PyTorch

TensorFlow

XGBoost

Sklearn

Java

C++

Tools

PyTorch

TensorFlow

Spark MLlib

XGBoost

Scikit-learn

Job description

Yelp is seeking a Staff-level ML Engineer to join the Content Contributor Intelligence team. You will build ML systems end-to-end—from training models to deployment—using neural networks, LLMs, and embeddings for text, images, and videos.

You will mentor engineers and collaborate across teams in a hybrid role that welcomes applicants from multiple locations. You will work with PyTorch, TensorFlow, Spark MLlib, XGBoost and Scikit-learn, contributing to ML platforms and best practices across the