Machine Learning Engineer, Recommendations

Where we Work

Udemy is a global company headquartered in San Francisco, with additional U.S. offices in Denver and Austin, and international hubs in Australia, India, Ireland, Mexico, and Türkiye. T his is an in-office position, requiring three days a week in the office (Tuesday, Wednesday, Thursday) and flexibility on Mondays and Fridays .

About this role

Udemy is looking for a machine learning engineer to join our Recommendations Team. Udemy’s personalized recommendations system is composed of batch (e.g., feature and machine learning pipelines), streaming (i.e., feature computation in real-time), and online (i.e., microservices to serve personalized recommendations) components. The team is responsible for all these components, as well as the underlying algorithms and evaluation methodologies. 

In this role, you will design, build, and integrate scalable systems, platforms, and tools to provide better recommendations and personalization data with low latency. You will work in a wide stack, including both data-related technologies and backend development, as a part of a cross-functional agile team of engineers, data scientists, and product managers.

Are you passionate about learning, willing to take technical challenges, care about making an impact, and looking for a diverse, collaborative, and fun environment that also values quality? If so, come help us improve lives through learning at Udemy!

What you'll be doing

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  Design, develop, test, and deploy recommendation- and personalization-related systems, platforms, and tools at scale.



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  Develop performant ML models for realtime use-cases.



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  Design and analyze experiments and monitor performance of personalized recommendations.



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  Translate data science prototypes into scalable production implementations.



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  Partner with data scientists to troubleshoot and optimize complex data pipelines.



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  Collaborate with data scientists, engineers, and product managers to identify opportunities to improve our platform through personalization.

What you'll have

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  1-2 years of full-time experience with data science or backend engineering or equivalent.



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  Strong knowledge of computer science fundamentals, including object-oriented design, data structures, and algorithms.



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  Strong knowledge of statistics, machine learning, and natural language processing.



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  Proficiency with Python, Java and/or Kotlin.



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  Experience building complex data products.



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  Experience with offline data storage and processing frameworks such as Hadoop, Hive and Spark.



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  Self-driven, highly motivated, and able to learn quickly.



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  Excellent written and oral communication skills.

Preferred, but not required

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  Experience with the design, development, and operational maintenance of large-scale platforms and services.



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  Experience with recommendations, search or information retrieval.



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  Experience with online data storage and processing frameworks such as Kafka, MySQL, Redis, and DynamoDB.



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  Experience with SQL performance tuning and query optimization.