Staff Machine Learning Engineer(Platform - Identity)
We are looking for a talented Staff ML Engineer to enhance the intelligence layer of Coinbase's core Platform Identity services. You will be pivotal in developing, deploying, and maintaining machine learning models that secure and streamline user authentication, verification, and platform access control. Key responsibilities include working with large, sensitive datasets to train models related to fraud detection, identity verification (KYC/AML), and personalized security recommendations. This role demands a blend of deep ML theory knowledge and robust software engineering skills; you must build production-grade, scalable systems around these models. Proficiency in Python, familiarity with cloud MLOps tools, and experience handling privacy-sensitive data are essential. Success in this role means improving the overall robustness and usability of our platform for millions of global users.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
2500
Question set
180
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for Staff Machine Learning Engineer(Platform - Identity)?
The closing date is listed in the official advertisement — apply as early as possible.
How do I apply for Staff Machine Learning Engineer(Platform - Identity)?
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 coinbase 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(Platform - Identity) 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 Engineering, Identity & Access Management (IAM), NLP/NLU, Python/Scala.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.