Senior Model-Based RL Scientist (Remote UK)

Senior Model-Based RL Scientist (Remote UK)

Full-Time 63000 - 77000 Β£ / year (est.) Working from home possible
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At a Glance

  • Tasks: Design and evaluate AI agents for industrial control using model-based reinforcement learning.
  • Company: Phaidra, a fully remote company focused on innovative AI solutions.
  • Benefits: Equity options, global collaboration, and the chance to work remotely.
  • Other info: Join a dynamic team with opportunities for growth and innovation.
  • Why this job: Make a real impact on production-scale systems in cutting-edge AI research.
  • Qualifications: PhD required with strong Python/PyTorch skills and experience in safe RL.

The predicted salary is between 63000 - 77000 Β£ per year.

Phaidra is a fully remote company hiring a Senior AI Research Scientist to advance model-based reinforcement learning for industrial control. You will design and evaluate agents, develop world models, and guide research from concept to production. A PhD and strong Python/PyTorch skills are required, with experience in planning, safe RL, and sim-to-real.

Equity and global collaboration are offered. Remote across regions, with onboarding and opportunities to impact production-scale systems in a

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Senior Model-Based RL Scientist (Remote UK) employer: Phaidra

Phaidra is an exceptional employer that fosters a fast-paced, team-oriented environment where your contributions directly influence the company's trajectory. With a fully remote work culture, competitive compensation, and a strong emphasis on professional development, employees enjoy unlimited paid time off and flexible stipends to enhance their workspace and well-being. The company values transparency, collaboration, and empathy, ensuring a supportive atmosphere for all team members, regardless of their location.

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Contact Details:

Phaidra Recruitment Team

We think you need these skills to ace Senior Model-Based RL Scientist (Remote UK)

Model-Based Reinforcement Learning
Python
PyTorch
Planning
Safe Reinforcement Learning
Sim-to-Real Transfer
Agent Design and Evaluation