Job Openings Machine Learning Engineer

About the job Machine Learning Engineer

About Logically

Logically combines advanced AI, expert OSINT investigators and one of the world's largest dedicated fact-checking teams to fight damaging information and deliberate disinformation at scale. Logically builds technology and tools that empower governments, businesses and the public to identify and mitigate harmful content, in the hope to shape a more positive and cohesive social discourse.

Key Responsibilities: 

As an NLP/ML intern, you will be part of Logically's AI data science team. You will work with large scale data sets built in-house as well as open-source to experiment and evaluate state-of-the-art NLP/ML models. Further, you will have access to cutting-edge infrastructure, resources to build novel NLP/ML models and test their scalability. You will collaborate with data scientists and engineers to contribute to the development of impactful AI-based solutions which will be used across different products built to offer insights about media intelligence, Veracity, problematic content detection, threat intelligence, brand safety management, etc...

Must-have requirements:

  • Pursuing an advanced degree (MSc/Ph.D.) in a numeric discipline (e.g., Statistics, Machine Learning, Computer Science).
  • Experience in doing an ML project that is equivalent to an MSc research project
  • Experience in core ML and text analytics tasks and application areas (e.g., text classification, topic detection, information extraction, Named Entity recognition, entity resolution, Question-Answering, dialogue systems, chatbots, sentiment analysis, event detection, language modelling).
  • Good oral and written communication skills
  • Scientific expertise and real-world experience in Deep Learning. Hands-on experience with deep neural nets such as BERT, ELMO, XLNet, GPT, etc will be a bonus.
  • Good expertise in programming (e.g., Python, C++ or Java/Scala)
  • Familiarity with existing deep / machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Familiarity with existing Open Source NLP libraries and utilities (e.g., Stanford CoreNLP, spaCy,fastText, AllenNLP, PyTorch-NLP, Gensim, word2vec, GloVe).

Nice to Have:

  • Academic publications in premier AI/ML conferences in fundamental machine learning
  • Contributions to real-world data science and machine learning tools and libraries.
  • Lead ML projects from the prototype stage to the production stage

General Info:

Positons available: 2
Location: United Kingdom
Intended start date: ASAP
Duration: 6 months
Compensation: £25,000-£30,000 per year