Machine learning, AI research scientist
We are seeking a Machine Learning Scientist / Research Engineer (Deep Neural Networks) to join us at the beginning of our journey and work closely with the inventor of the technology. Ideally you will have a focus on complex, pioneering methods within Machine Learning such as Markov Models, Gaussian Process, Deep Learning & Reinforcement Learning. As a Machine Learning Scientist / Research Engineer, you will be learning and developing our algorithms and applying them to real life problems - the potential outcomes of these projects are truly exciting and we believe we can solve many problems which solutions have previously been unattainable with modern day tech.
- Work on complex data sets from some of the world’s largest organisations
- Use Maths, Stats and Machine learning to derive key insights across various industry sectors including pharma, health and high-tech.
- Flexible working environment - to be a part of a team with no red tape or bureaucracy
- An advanced environment in which you can utilize, and learn, the newest and most innovative research in Machine learning, Deep Neural Networks, Reinforcement Learning
- A chance to work on Machine Learning problems and technology which has the potential to scale upon a global level.
- Competitive remuneration – inc travel and expenses
- Educated to an MSc or PhD level in the field of Computer Science, Machine Learning, Applied Statistics, Mathematics
- Ability to clearly communicate the designed algorithms, data flows and outcomes
- Highly motivated self-start with strong delivery of results.
- Experience in statistical modelling and machine learning
- Flexible, adaptable and pro-active with a ‘can-do’ approach.
- Familiar working in Unix environments, and experienced in working in 3 of Fortran, GPU optimization, Java, C++, Python, Scala, GoLang, Docker, AWS, REST API’s, Spark, Hadoop
Nice to have
- Knowledge of distributed computing or NoSQL technologies is a bonus
- Proven commercial application of advanced analytical and statistical methods