The Machine Learning team is expanding and we are searching for an experience Machine Learning/ AI Engineer. They are specifically interested in neural networks (Artificial Neural Networks, Convolutional Neural Networks, Recurrent neural network), Deep Learning machine learning techniques (both supervised and unsupervised) or natural language processing techniques (NLP)
You will help to build the trading systems of the future by researching, developing and implementing predictive algorithms to analyse collections of financial market and non-financial data, both structured and unstructured.
The role will involve implementation, optimisation and development of extensive and varied data sets from both global financial markets and non-financial market sources; supporting a Greenfield business initiative in a world leading Hedge Fund in London, developing robust forecasting models using a portfolio of machine learning and other signal processing, statistical and econometric techniques; applying these in the development and implementation of new trading, execution, risk management and portfolio construction applications.


GQR Global Markets Company Location


PhD or PhD candidate in machine learning, computer science, statistics, or a related field.
Substantial deep learning research experience in an area such as computer vision, natural language processing or generic machine learning
Practical, “hands-on” experience in one or more deep learning technologies such as ANNs, CNNs, RNNs, and LSTMs
Superb analytical and quantitative skills, along with a healthy streak of creativity.
A leader within the research field shown with peer reviewed publications such as with IEEE
Demonstrated ability to conduct independent research utilizing large data sets.
Passion for seeing research through from initial conception to eventual application.
Curiosity about financial markets.
Strong scientific programming in Python, R or Matlab.

Educational level:

Ph. D.

How to apply:

Please mention NLP People as a source when applying


Tagged as: , , , , ,