Machine Learning Engineer (Gaussian Processes, Neural Nets)

Understanding Recruitment
14 Feb 2018
15 Mar 2018
Contract Type
Full Time
Job Description

Machine Learning Engineer (Gaussian Processes, Neural Nets):

We are currently seeking a Machine Learning Engineer (Gaussian Processes, Neural Nets) for an 18 month-old company based in central Cambridge with Tier One VC backing, currently creating a platform that is set to transform Machine Intelligence. This platform will work to disrupt modern approaches to smart-cities, gaming and self-driving vehicles.

We're looking for a Machine Learning Engineer who is willing to take on a hands-on approach and bring their own relevant Machine Learning experience. Within this organization, you will have opportunity to develop a decision-making agent that can be implemented in extremely complex environments. We are looking for Machine Learning Engineers from all types of backgrounds, from experience in Natural Language Processing, Gaussian Processes, Deep Learning, Computer Vision etc.

As a Machine Learning Engineer you will be a core member of the machine learning team; working closely with the Machine Learning researchers, as well as the Software Developers. You will be implementing your unique skills within the domain and be a key voice within the company.

What we can offer a Machine Learning Engineer (Gaussian Processes, Neural Nets):

  • To work in a high profile company who have secured another round of funding
  • To work in an organization with a startup culture
  • Strong salary and package
  • Flexible working environment
  • To work on the most exciting tech within the Machine Learning domain
  • To work amongst world leading researchers and engineers

Key Skills: Machine Learning Engineer; Tensorflow, C++, C, Java, Python, C#, Distributed Algorithms. Distributed systems, BSc, MSc, MPhil, PhD, Post-Doc, Research, R&D, startup, Multithreading. Machine Learning, AI, Artificial Intelligence, NLP, Natural Language Processing, Linguistics, Computational Biology, Computational Linguistics, Reinforcement Learning, Multi-Agent Systems, Deep Learning, Bayesian Inference, Probabilistic Models.

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