Machine Learning Engineer - Foundational
Harmattan-AI is seeking a talented Machine Learning Engineer to join our innovative R&D team. This foundational role is critical for developing and refining core artificial intelligence models that power our next generation of solutions. Responsibilities include the entire ML lifecycle: from ideation and data preprocessing to model training, optimization, and deployment. You will work hands-on with large datasets using advanced deep learning frameworks (PyTorch/TensorFlow) and Python. Ideal candidates possess strong academic backgrounds in AI/CS and practical experience developing scalable, high-performance machine learning systems. This is an on-site role based in Paris, offering immense opportunities to shape the future of applied ML technology.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
2100
Question set
150
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for Machine Learning Engineer - Foundational?
The closing date is listed in the official advertisement — apply as early as possible.
How do I apply for Machine Learning Engineer - Foundational?
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 harmattan-ai 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 Machine Learning Engineer - Foundational 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, Deep Learning, Python, TensorFlow/PyTorch.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.