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bjakcareer

Staff Machine Learning Engineer

bjakcareer is seeking an accomplished and driven Staff Machine Learning Engineer to join our ActAI Engineering department. This role is central to developing next-generation AI solutions, focusing particularly on natural language processing (NLP) and advanced machine learning models. Key responsibilities include designing, implementing, and deploying scalable ML pipelines from concept to production. You will collaborate with cross-functional teams to solve complex business challenges using state-of-the-art techniques. The ideal candidate must have deep expertise in Python, PyTorch/TensorFlow, and experience optimizing large-scale models for real-world deployment. This fully remote position offers the chance to work on impactful projects that shape the future of our products.

bjakcareer Indonesia (Remote), Indonesia

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

Preparing

1500

Question set

200

Duration

Permanent

Focus areas

Machine LearningNLPDeep LearningPythonAI/ML Engineering

People Also Ask (FAQs)

What is the last date to apply for Staff Machine Learning Engineer?

The closing date is listed in the official advertisement — apply as early as possible.

How do I apply for Staff Machine Learning 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 bjakcareer 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 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, NLP, Deep Learning, Python, AI/ML Engineering.

Quick start

Review the job summary, then jump into prep with the same flow used for exam starts.

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