We are looking for reinforcement learning engineers to join our team. We are interested in individuals with knowledge and experience in developing and evaluating different RL architectures and algorithms, and in their applications in experimentation and causal inference. You will have the unique opportunity of, together with our data science and engineering teams, defining and building from scratch reinforcement learning features for our machine learning platform for equitable healthcare.
Reinforcement Learning Engineer
If you are passionate about using RL to understand causality in real life systems, and are excited about using your skills to improve the world, we want to meet you!
- Working with the engineering and data science teams in the reinforcement learning algorithms that will run in our platform: definition, initial implementation, evaluation and deployment
- Formulate real-world causal inference and experimental design problems in terms of different RL algorithms, and independently conduct research to decide upon the best approach
- BSc/BEng degree in computer science, mathematics, physics, electrical engineering, machine learning or related fields; or equivalent technical proficiency
- Experience in reinforcement learning and its libraries (Keras-rl, TF-agents, garage, Pyqlearning, ChainerRL…)
- Solid coding skills in at least one of the following languages: Go, Java, Scala, C/C++, Python
- MSc/MEng or PhD in computer science, mathematics, physics, electrical engineering, machine learning or related fields; or equivalent research experience
- Knowledge of RL applications in healthcare
- Experience using a variety of RL algorithms: actor-critic, policy gradient, DQN, VFA, SARSA, Q-learning, model-based and model-free Monte Carlo, dynamic programming...
- Experience with causal inference and experimental design
- Experience with deep learning and its frameworks (TensorFlow, Keras, Caffe, Theano...)
- Experience with recommendation systems and/or natural language processing
- Strong statistical background
- Experience with machine learning lifecycle management tools (e.g. mlflow)
- Experience with continuous deployment of models with build pipeline
- Experience with databricks
- Experience with Spark performance tuning, data pipeline testing and MLlib