Staff Machine Learning Engineer, Home Surfaces
Join the core engineering team to drive the personalization experience across Spotify's home surfaces. As Staff ML Engineer, you will be responsible for developing and deploying advanced recommendation algorithms that predict user intent and improve content discovery on key landing pages. Key responsibilities include working with streaming data, defining feature stores, optimizing model serving latency, and iterating rapidly based on A/B test results. The ideal candidate is a seasoned practitioner of ML in the recommendation space who deeply understands user behavior modeling. Experience with large-scale graph databases or collaborative filtering techniques is essential. This role demands technical leadership—owning complex technical domains while maintaining best practices for production machine learning systems.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
3200
Question set
280
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for Staff Machine Learning Engineer, Home Surfaces?
The closing date is listed in the official advertisement — apply as early as possible.
How do I apply for Staff Machine Learning Engineer, Home Surfaces?
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 spotify 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 Staff Machine Learning Engineer, Home Surfaces 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, Personalization, Recommendation Systems, Data Modeling.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.