Senior AI Research Scientist (Model-based RL)

Role Description

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.

• 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 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 define new research directions.
• Translate research into practical outcomes.
• Own the development and rollout for an entire research area or large project.

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
  • World models / learned dynamics surrogates
  • Reinforcement Learning and Deep Learning
  • Control Theory
  • Safe / constrained RL
• 2+ years of research experience in academia or industry after PhD graduation.
• Extensive research in the fields of {ModelBased, ModelFree, Safe}RL and Control Theory, with particular depth in model-based methods.
• Hands-on experience building and evaluating agents against simulators (e.g. differentiable simulators or world models) and closing the sim-to-real gap.
• Share our company values: Collaboration, Transparency, Operational Excellence, Ownership, and Empathy.

Requirements

• 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 (e.g. Ray, Kubernetes, GCP).
• A proven track record of publications in RL, control, or a related area.
• A real passion for AI and applying it to industrial systems to improve resource efficiency.

Benefits

• Fast-paced, team-oriented environment where your work directly shapes the company’s direction.
• We are a 100% remote company.
• Competitive compensation & meaningful equity.
• Outsized responsibilities & professional development.
• Training is foundational; functional, customer immersion, and development training.
• Medical, dental, and vision insurance (exact benefits vary by region).
• Unlimited paid time off, with a required minimum of 20 days per year.
• Paid parental leave (exact benefits vary by region).
• Flexible stipends to support your workspace, well-being, and continued professional development.
• Company MacBook.