Design, implement, and evaluate model-based reinforcement learning agents — including planning-based controllers (MPC, MPPI) — and the software prototypes needed to deploy them on real industrial control systems.

Develop learned dynamics and world models (learned surrogates) that generalize across systems, including the training pipelines — pretraining, curriculum learning, active/adversarial learning, and fine-tuning — needed to make them reliable for planning and control.

Research and implement methods for e.g. safe RL, constrained control, scenario planning and Bayesian RL, to develop agents that satisfy safety constraints during deployment.

Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.

Participate in and organize ambitious collaborative research projects, and work with external collaborators and partners to translate research into production outcomes.

Mentor and guide Research Engineers to apply research findings and developments to industrial domains.

Independently defines new research directions

Translates research into practical outcomes

Owns the development and rollout for an entire research area or large project

Key Qualifications

PhD in a technical field or equivalent practical experience, with a strong background in model-based reinforcement learning and demonstrated knowledge in one or more of the following:

Planning algorithms

Senior AI Research Scientist (Model-based RL)

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Phaidra

More roles at Phaidra