ML Software Tool Development Engineer
As an ML Software Tool Development Engineer at Cerebras, you will play a critical role in building and optimizing the foundational software toolchains that power our cutting-edge AI inference solutions. This position requires expertise in developing scalable, high-performance tools for machine learning workflows, from model optimization to deployment within specialized hardware environments. Key responsibilities include architecting robust CI/CD pipelines for ML tooling, implementing performance profiling mechanisms across various compute substrates, and collaborating with core infrastructure teams. The ideal candidate possesses strong software engineering fundamentals, deep knowledge of Python and C++, and experience working with large-scale distributed systems. You will ensure that our internal ML toolset is efficient, reliable, and capable of meeting the demands of state-of-the-art AI models.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
1800
Question set
250
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for ML Software Tool Development Engineer?
The closing date is listed in the official advertisement — apply as early as possible.
How do I apply for ML Software Tool Development Engineer?
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 Cerebras Systems, Inc. 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 ML Software Tool Development Engineer 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, Software Development, Toolchains, Core ML Infrastructure.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.