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Open nowFirst seen 22 hours ago

Machine Learning Engineer

Global One109 open roles

Where
Cape Town, Western Cape, South Africa, Remote
Work mode
Remote
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Your applicationOpen nowMachine Learning EngineerGlobal One · Cape Town, Western Cape, South Africa, Remote

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The clock on this job

Early applications get read.

15.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 6.5%3 days
  3. 15.1%7 days
This job: first seen 22 hours ago

The posting

Job brief

We are actively searching for a talented and experienced Machine Learning (ML) Engineer to join our team.

As a Machine Learning Engineer, you will play a crucial role in the development and implementation of cutting-edge artificial intelligence products.

Your responsibilities will involve designing and constructing sophisticated machine learning models, as well as refining and updating existing systems.

In order to thrive in this position, you must possess exceptional skills in statistics and programming, as well as a deep understanding of data science and software engineering principles.

Your ultimate objective will be to create highly efficient self-learning applications that can adapt and evolve over time, pushing the boundaries of AI technology. Join us and be at the forefront of innovation in the field of machine learning.

Requirements

Proven experience as a Machine Learning Engineer or similar roleUnderstanding of data structures, data modeling and software architectureDeep knowledge of math, probability, statistics and algorithmsAbility to write robust code in Python, Java and RFamiliarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)Excellent communication skillsAbility to work in a teamOutstanding analytical and problem-solving skillsBSc in Computer Science, Mathematics or similar field; Masters degree is a plus

Responsibilities

Study and transform data science prototypesDesign machine learning systemsResearch and implement appropriate ML algorithms and toolsDevelop machine learning applications according to requirementsSelect appropriate datasets and data representation methodsRun machine learning tests and experimentsPerform statistical analysis and fine-tuning using test resultsTrain and retrain systems when necessaryExtend existing ML libraries and frameworksKeep abreast of developments in the field

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