Machine Learning Engineer / Reinforcement Learning (from 30hr/week)

Apply cutting-edge research in Artificial Intelligence and Reinforcement Learning (RL) to real-world optimization and decision problems. Work on high-impact applications such as next-generation smart power grids.

• Contribute building blocks and methodology based on our open-source framework Maze, focused on simulation-based RL, together with an exceptional team of Research Scientists, Machine Learning Engineers and Infrastructure Experts


• Analyze industry-related optimization and decision problems and translate them into Machine Learning systems (covering the complete development cycle from simulation engineering up to agent development, training and deployment)


• Design and implement proof of concepts, develop prototypes in Python and PyTorch for both RL-ready simulations (environments) as well as RL agents


• Review state-of-the-art literature and judge its relevance for practical applicability

• Excellent object oriented Python programming skills, knowledge of PyTorch and a wide range of Machine Learning methods


• Background in the field of RL and related Machine Learning disciplines


• Basic knowledge about containerization and distribution technologies like Docker, Kubernetes and Ray


• Passionate about all things AI, Machine Learning and Data Science


• Self-driven problem solver who enjoys coding as much as tinkering


• Ability to work independently as well as part of a team


• Degree in computer science (BSc/MSc) or in a similar field


• Valid work permit for Austria

• Enthusiastic, research-driven team with rich expertise in Reinforcement Learning and Computer Vision as well as distributed training, data engineering, ML ops and cloud architecture


• Working with the latest technologies at the interface between research and industry (enliteAI is an ELISE EU research network Organizing Node)


• Flexible work models: Remote work, an office in Vienna's 1st district and minimal core hours


• Budget and time allotment for the pursuit of individual R&D projects, training or conference participations