Companies Hiring Machine Learning Engineers in London
Google UK
Requirements:
- Master’s or Ph.D. in Computer Science or related field
- Proficiency in Python, TensorFlow, or PyTorch
- Strong mathematical and statistical skills
- Experience with machine learning frameworks and libraries
Apply Here – Careers
Facebook London
Requirements:
- Bachelor’s degree in Computer Science or equivalent
- Knowledge of deep learning techniques and NLP
- Experience with large-scale data processing and analysis
- Strong problem-solving and critical thinking abilities
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Amazon UK
Requirements:
- Master’s degree in Machine Learning, AI, or related field
- Proficiency in programming languages like Python, Java, or Scala
- Experience with cloud platforms (AWS, Azure, Google Cloud)
- Familiarity with data mining and pattern recognition algorithms
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Microsoft UK
Requirements:
- Strong background in machine learning algorithms and data modeling
- Experience with Azure ML, Spark, or Hadoop
- Knowledge of software development lifecycle (SDLC) practices
- Excellent communication and teamwork skills
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IBM UK
Requirements:
- Bachelor’s or Master’s degree in Computer Science or AI
- Proficiency in machine learning frameworks (e.g., scikit-learn, Keras)
- Understanding of big data technologies (Hadoop, Spark)
- Ability to work in agile development environments
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DeepMind
Requirements:
- Advanced degree (MSc or Ph.D.) in Machine Learning, AI, or related field
- Expertise in deep learning frameworks (TensorFlow, PyTorch)
- Strong programming skills in Python or C++
- Experience with large-scale data processing and distributed computing
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Twitter UK
Requirements:
- Bachelor’s or Master’s degree in Computer Science or AI
- Proficiency in machine learning algorithms and statistical modeling
- Experience with natural language processing (NLP) and sentiment analysis
- Familiarity with cloud platforms (AWS, GCP)
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Uber UK
Requirements:
- Strong background in machine learning and data science
- Proficiency in programming languages like Python, R, or Java
- Experience with big data technologies (Hadoop, Spark)
- Ability to work in a fast-paced environment and solve complex problems
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Deliveroo
Requirements:
- Degree in Computer Science, Mathematics, or related field
- Knowledge of machine learning algorithms and data analysis
- Experience with cloud services and deployment of ML models
- Passion for innovation and improving user experience
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Revolut
Requirements:
- Master’s degree in AI, Machine Learning, or equivalent
- Expertise in statistical analysis and predictive modeling
- Proficiency in Python, R, or similar languages
- Experience with data visualization tools and techniques
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Improbable
Requirements:
- Bachelor’s or Master’s degree in Computer Science or AI
- Strong knowledge of machine learning algorithms and techniques
- Experience with distributed systems and parallel computing
- Ability to work in a collaborative and innovative environment
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Bloomberg
Requirements:
- Advanced degree in Computer Science, AI, or related field
- Proficiency in machine learning frameworks (e.g., scikit-learn, TensorFlow)
- Experience with data mining, feature engineering, and model evaluation
- Strong analytical and problem-solving skills
Apply Here – Careers
Quantexa
Requirements:
- Degree in Computer Science, Mathematics, or Engineering
- Knowledge of machine learning algorithms and data processing techniques
- Experience with big data platforms (Hadoop, Spark)
- Ability to work on end-to-end ML projects
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Monzo
Requirements:
- Bachelor’s or Master’s degree in Computer Science or AI
- Knowledge of machine learning techniques and algorithms
- Experience with data preprocessing, feature selection, and model deployment
- Passion for fintech innovation and customer-centric solutions
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Just Eat Takeaway.com
Requirements:
- Degree in Computer Science, Mathematics, or related field
- Strong understanding of machine learning concepts and algorithms
- Experience with cloud platforms and scalable ML solutions
- Ability to work in a dynamic and fast-growing environment
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TransferWise
Requirements:
- Master’s degree in AI, Machine Learning, or equivalent
- Expertise in statistical modeling, optimization, and ML frameworks
- Proficiency in Python, SQL, and data visualization tools
- Experience with building scalable ML pipelines and deployment
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Skyscanner
Requirements:
- Bachelor’s or Master’s degree in Computer Science or AI
- Knowledge of machine learning algorithms and data analysis
- Experience with recommendation systems and personalization
- Strong problem-solving and critical thinking abilities
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Arm
Requirements:
- Advanced degree in Machine Learning, AI, or related field
- Proficiency in programming languages (Python, C++)
- Experience with deep learning frameworks and edge computing
- Ability to innovate and contribute to cutting-edge ML solutions
Apply Here – Careers
Splunk
Requirements:
- Degree in Computer Science, Mathematics, or Engineering
- Strong background in machine learning algorithms and data science
- Experience with anomaly detection, clustering, and classification
- Ability to work with unstructured data and develop scalable ML solutions
Apply Here – Careers
Machine Learning Engineer Jobs in London
London as a multifaced dynamic technological field and a location of very diverse variety of cultures presents an extraordinary pool of Machine Learning Engineer’s opportunities. Such experts become a critical factor in devising sophisticated AI products, study of data and algorithms for spotting patterns. This article guides those who are interested in becoming Machine Learning Engineers in London with information about companies hiring, websites to post your CV on, and the latest trends in salaries and questions you may have.
Major roles and responsibilities of a Machine Learning Engineer involve:
- Develop machine learning models and algorithms.
- Collect and clean data for analysis.
- Decent machine-learning techniques and algorithms must be chosen for individual tasks.
- Datasets are used for training and tuning of models.
- Evaluate model performance and how correctly it predicts.
- Decide on what models to take into the production environments.
- Continue with monitoring and keeping an eye on deployed models for performance and reliability.
- Provide collaboration across data scientists, software engineers, and domain experts.
- Keep yourself updated with the recent developments of machine learning technologies and its tools.
- Present results of work extensively to stakeholders in a manner that is easy to follow.