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harmattan-ai

Machine Learning Engineer - Foundational

Join Harmattan-AI as a Machine Learning Engineer tasked with developing and scaling foundational AI models. This role is central to our R&D efforts in Zurich, requiring expertise across the entire ML stack. Key responsibilities involve building robust deep learning architectures using industry-standard tools like PyTorch or TensorFlow. You will be expected to manage complex data pipelines, perform rigorous model validation, and collaborate with senior scientists to transition research prototypes into production-grade systems. Candidates must possess a strong background in statistical modeling, software engineering best practices, and advanced mathematical concepts. This is an on-site full-time position in Zurich, offering exposure to challenging international AI problems.

harmattan-ai Zurich, Switzerland

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

Preparing

1850

Question set

200

Duration

Permanent

Focus areas

Machine LearningDeep LearningPythonTensorFlow/PyTorch

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.

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