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Machine Learning Engineer

Job in Greater London, London, Greater London, EC1A, England, UK
Listing for: Global One
Full Time position
Listed on 2026-01-13
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer, Data Scientist, Data Engineer
Job Description & How to Apply Below
Location: Greater London

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 role
  • Understanding of data structures, data modeling and software architecture
  • Deep knowledge of math, probability, statistics and algorithms
  • Ability to write robust code in Python, Java and R
  • Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
  • Ability to work in a team
  • Outstanding analytical and problem-solving skills
  • BSc in Computer Science, Mathematics or similar field;
    Masters degree is a plus
Responsibilities
  • Study and transform data science prototypes
  • Design machine learning systems
  • Research and implement appropriate ML algorithms and tools
  • Develop machine learning applications according to requirements
  • Select appropriate datasets and data representation methods
  • Run machine learning tests and experiments
  • Perform statistical analysis and fine-tuning using test results
  • Train and retrain systems when necessary
  • Extend existing ML libraries and frameworks
  • Keep abreast of developments in the field
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