Reinforcement Learning Engineer

Key Skills

C Programming, C++, Caffe, Data pipelines, Databricks, Deep Learning, Java Programming, Keras software library, Machine learning techniques, MLflow, Python Programming, Reinforcement Learning, Scala Programming, TensorFlow

Job Description

Responsibilities

• 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

Minimum qualifications

• 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

Preferred qualifications

• 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.