Job Title

Machine Learning Engineer – Recommender Systems (NLP)

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Charlie Ford

Data Science & AI Lead Consultant

  • Machine Learning Engineer – Recommender Systems (NLP)

    £ 70K - 100K
    Job ID: 03149

    My client, a well-established organisation that develops publishing technologies for getting mission critical content into the hands of the practitioners and researchers who need it most.
    They are actively seeking an experienced Machine Learning Engineer to come on board and become a focal point of the tech team in Oxford.
    The ideal candidate will have a deep understanding of recommender systems with proven practical application experience. The role involves collaboration with software engineers, research scientists and product teams to design and develop the next generation of the companies’ recommender systems, to help researchers and scientists know more and achieve more.

    Role Responsibilities:
    • Exploring, designing and developing effective large-scale commercial recommender systems, analysing contextual and behavioural real-world data.

    • Evaluating and optimizing algorithms through feature engineering, controlled offline and online A/B testing experiments.

    • Optimizing recommended system stability, architecture scalability, ensuring rapid iteration of algorithm strategy modules. This is an exciting and challenging role, where you will not only have the opportunity to collaborate with worldwide reputational research labs, but also continuously learn and apply cutting-edge technologies in academia and industry.

    Required Skills

    • Hands on experience with design, development and deployment of large-scale commercial information retrieval and recommender systems
    • Deep understanding and experience in Text Mining and Natural Language Processing (NLP) in a commercial setting
    • Hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages
    • Extensive practical experience in manipulating large scale data (data cleaning, data normalization, data linkage)
    • PhD with at least 3 years, or MSc with at least 5 years relevant commercial experience

    Desirable

    • Experience with big data technologies (e.g. Apache Spark, Apache Flink, Apache Kafka, Elasticsearch, Apache Solr, Hadoop, MapReduce, Scalding/Cascading)
    • Familiar with search algorithms and core technologies, including Query understanding, Recall and ranking algorithms etc
    • Familiar with frameworks such as Keras, TensorFlow, PyTorch, sklearn, liblinear, Weka, OpenNLP, etc
    • Familiar with cloud technologies, such as AWS, Google AI Cloud, etc
    • Deep understanding of popular collaborative filtering / behavioural data analysis and content-based recommendation algorithms and solutions

    Benefits

    • Health Insurance
    • Private Pension
    • Good holiday allowance
    • Opportunity to share ideas
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