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Machine Learning Engineer – Feed Recommendation

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Onsite

Machine Learning Engineer – Feed Recommendation

AppLovin

Machine Learning Engineer – Feed Recommendation

Skills

PythonPyTorchTensorFlowMachine LearningAI

2 days ago

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Important information


Contract type:

Permanent

Location:

Singapore

Work mode:

Onsite

Published on:

18 October 2026

What they need


About AppLovin AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end advertising solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com.

To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others.

【The Role】 We are looking for a Machine Learning Engineer with strong experience in large-scale recommendation systems to help build the next-generation social media platform.

You will own critical components of our recommendation stack — including recall, ranking, CTR modeling, and multi-objective optimization — with the goal of driving retention, engagement, and long-term ecosystem growth.

【A Day in the Life】

  • Design and deploy scalable recommendation pipelines
  • Develop and optimize CTR/CVR prediction models
  • Improve multi-objective ranking strategies (retention, monetization, diversity, long-term value)
  • Tackle cold-start challenges for new users and new content
  • Run offline experiments and online A/B testing to drive measurable gains
  • Collaborate closely with product, engineering, and monetization teams
  • Continuously iterate on model performance, latency, and system reliability

【The Impact You’ll Make】

  • Improve user retention through intelligent content recommendation
  • Drive measurable lift in engagement and monetization metrics
  • Build core ranking mechanics beyond incremental model tuning
  • Shape the foundation of a scalable, long-term content ecosystem

【Who You Are】

  • 3–5 years of experience building production-grade ML systems

  • Strong hands-on experience in recommendation systems

  • Experience in one or more:

  • Recall systems / candidate generation

  • Ranking models

  • CTR prediction

  • Multi-task or multi-objective optimization

  • Proficient in Python and ML frameworks (PyTorch, TensorFlow, etc.)

  • Strong software engineering fundamentals

  • Experience with large-scale data systems and distributed training is a plus

  • Experience improving retention or long-term user value is highly valued

【Why Join This Venture】

  • Build 0-to-1 systems inside a proven AI powerhouse
  • High ownership and direct business impact
  • Startup speed with AppLovin-level scale and resources
  • Opportunity to shape a new growth engine for the company

AppLovin is proud to be an equal opportunity employer that is committed to inclusion and diversity. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or other legally protected characteristics. Learn more about EEO rights as an applicant here.

If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send us a request at [email protected] AppLovin will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. If you’re applying for a position in California, learn more here.

To support an efficient and fair hiring process, we may use technology-assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers.

Please read our Global Applicant Privacy Notice to learn more about how AppLovin processes your personal information.

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