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airbnb

Senior Machine Learning Engineer, Trust

You will be a critical contributor in maintaining the trust and safety of the global Airbnb marketplace. This role focuses on developing and deploying advanced machine learning systems to detect, classify, and mitigate various forms of abuse, fraudulent listings, and policy violations. Key responsibilities include building sophisticated classifiers using NLP for content review, creating anomaly detection models for user behavior, and improving the reliability of moderation tools at scale. The ideal candidate has deep ML expertise, practical experience in real-world content safety issues, and a proactive mindset towards ethical AI deployment. You will collaborate cross-functionally with policy teams to ensure our technology keeps pace with evolving threats.

airbnb San Francisco, CA, United States

(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)

Preparing

1950

Question set

220

Duration

Permanent

Focus areas

Machine LearningAI SafetyContent ModerationNLP

People Also Ask (FAQs)

What is the last date to apply for Senior Machine Learning Engineer, Trust?

The closing date is listed in the official advertisement — apply as early as possible.

How do I apply for Senior Machine Learning Engineer, Trust?

Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.

Is there negative marking in the airbnb test?

The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.

What is the passing marks for this test?

The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.

How can I prepare for the Senior Machine Learning Engineer, Trust test?

Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: Machine Learning, AI Safety, Content Moderation, NLP.

Quick start

Review the job summary, then jump into prep with the same flow used for exam starts.

Prepare for this test

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