Senior AI Research Scientist (Model-based RL) - Phaidra

About Phaidra

Phaidra is building the future of industrial automation. We create AI-powered control systems for the industrial sector, enabling facilities to automatically learn and improve over time using reinforcement learning algorithms. Our team has a track record of applying AI to some of the toughest problems, from DeepMind's AlphaGo to reducing energy required to cool Google's data centers.

We are 100% remote with no physical office, with team members located globally.

Who You Are

Research Scientists at Phaidra lead our efforts in developing novel algorithmic architecture towards the end goal of bringing intelligent control systems to the industrial sector. You draw on expertise from disciplines including model-based reinforcement learning, planning and optimal control, deep learning, world models, and safe reinforcement learning.

Responsibilities

• 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 training pipelines for pretraining, curriculum learning, active/adversarial learning, and fine-tuning.
• Research and implement methods for safe RL, constrained control, scenario planning, and Bayesian RL.
• Report and present research findings and developments internally and externally.
• Participate in collaborative research projects and work with external collaborators to translate research into production outcomes.
• Mentor and guide Research Engineers to apply research findings to industrial domains.
• Independently define new research directions and own development for entire research areas.

Key Qualifications

• PhD in a technical field or equivalent practical experience, with a strong background in model-based reinforcement learning and demonstrated knowledge in planning algorithms, world models, RL and deep learning, control theory, or safe/constrained RL.
• Either 2+ years of research experience post-PhD or 5+ years post-Master’s.
• Extensive research in RL and control theory, with depth in model-based methods.
• Hands-on experience building and evaluating agents against simulators and closing the sim-to-real gap.
• Alignment with Phaidra's values: Agency, Velocity, Craft, & Truth.

Preferred Skills & Experience

• PhD in machine learning, control, or a closely related field.
• Deep, hands-on experience with model-based RL and planning agents applied to real-world dynamical or industrial systems.
• Strong Python and PyTorch skills, including vectorized/differentiable simulators and scaling experiments on distributed compute (Ray, Kubernetes, GCP).
• Proven track record of publications in RL, control, or related areas.

Our Stack

• Python
• PyTorch, scipy
• Kubernetes, Docker, Ray
• GCP