Senior Machine Learning Researcher in Reinforcement Learning

  • Prowler.io
  • Cambridge, UK
  • 26/02/2019
Full time Machine Learning Artificial Intelligence Statistics Software Engineering

Job Description

We are looking to hire exceptional scientists to join our reinforcement learning team. You will be contributing to our research work and developing algorithms that are both theoretically rigorous, yet scalable to domains approaching real-world complexity.

 Your responsibilities

  • Develop novel reinforcement learning algorithms
  • Conduct theoretical analysis of proposed algorithmic solutions
  • Contribute to research work and publications the team is working on
  • Develop and run large-scale experiments to identify effective solutions
  • Work closely with other researchers and engineers in an Agile team

Essential skills and experience

  • PhD in Computer Science, Electrical Engineering, or an equivalent degree in a related field
  • Strong background in the fields of reinforcement learning, and/or control theory
  • Strong knowledge of machine learning and artificial intelligence algorithms
  • Publications at top machine learning and robotics conferences, including but not limited to ICML, NeurIPS, AISTATS, UAI, AAAI, ICRA, RSS, IROS, ICLR, IJCAI
  • Publications in high-impact machine learning and robotics journals, including but not limited to JMLR, PAMI, JAIR, IJRR

Desired qualifications, skills and experience

  • Experience with any of the following topics is a big plus:
  • Model-based reinforcement learning
  • Variational inference and/or probabilistic modelling
  • Multi-task and/or lifelong learning
  • Experience using MuJoCo and/or OpenAI gym environments
  • Experience with Tensorflow and/or Pytorch libraries

Benefits :

  • Flexible working hours
  • Private health-care and dental cover
  • Company stock options
  • Relocation costs covered
  • Friday team lunches
  • Half-day on the last Friday of the month
  • Weekly games nights
  • Gym membership
  • Cambridge Botanic Garden membership

The location: Cambridge, United Kingdom. Candidates must be authorised to work in the UK