indeed
Entry Level Machine Learning Engineer (Remote - Canada)
Yelp
Remote, CAN
Full-time
Entry
Remote
$85,000 to $107,000,
Discovered 3 weeks ago
machine learning model development and productionizationmachine learning frameworks including TensorFlow, PyTorch, Spark MLlib, XGBoost, or scikit-learnPython or equivalent programmingJavaC++large language models
Free
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Ready to ScanKey skills for this role
machine learning model development and productionizationmachine learning frameworks including TensorFlow, PyTorch, Spark MLlib, XGBoost, or scikit-learnPython or equivalent programming
Role Overview
Yelp engineering teams build machine learning and artificial intelligence systems that connect users with local businesses.
The role covers the full machine learning lifecycle, including data pipelines, model training, production deployment, and generative AI applications.
The position is designed for an entry-level machine learning engineer and supports learning through managers, mentors, and engineering teams.
Key Skills for This Role
machine learning model development and productionizationmachine learning frameworks including TensorFlow, PyTorch, Spark MLlib, XGBoost, or scikit-learnPython or equivalent programmingJavaC++large language models
Full Job Posting
Summary
Yelp engineering teams build machine learning and artificial intelligence systems that connect users with local businesses.
The role covers the full machine learning lifecycle, including data pipelines, model training, production deployment, and generative AI applications.
The position is designed for an entry-level machine learning engineer and supports learning through managers, mentors, and engineering teams.
What You Will Do
- Work on machine learning problems such as ad personalization, search ranking, Voice AI, chatbots, churn prevention, clickstream analytics, classification, recommendations, and bot detection.
- Collaborate with software engineers, applied scientists, and product managers to identify and use relevant consumer and business data.
- Balance scale, latency, cost, and availability according to the problem.
- Build data pipelines, train models, deploy systems in production, and deploy generative AI applications.
What It Takes to Succeed
- Experience developing and productionizing machine learning models with their supporting data pipeline.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, Spark MLlib, XGBoost, or scikit-learn.
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