Senior AI Research Scientist, Model-based RL

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Design, develop, and assess model-based reinforcement learning agents, which include planning-based controllers such as MPC and MPPI, along with the software prototypes essential for their deployment in real industrial control systems.

• Create learned dynamics and world models (learned surrogates) that can generalize across various systems, encompassing the training pipelines—such as pretraining, curriculum learning, active/adversarial learning, and fine-tuning—necessary to ensure their reliability for planning and control.

• Investigate and apply methods such as safe RL, constrained control, scenario planning, and Bayesian RL to create agents that meet safety constraints during deployment.

• Communicate and present research findings and developments, including status updates and results, clearly and effectively to both internal and external audiences, in both spoken and written formats.

• Engage in and organize ambitious collaborative research initiatives, cooperating with external collaborators and partners to convert research into practical outcomes.

• Mentor and support Research Engineers in applying research findings and advancements to industrial applications.

• Independently establish new research pathways.

• Convert research into actionable results.

• Take ownership of the development and implementation for an entire research domain or a significant project.

⛳️ Requirements

• PhD in a technical discipline or equivalent practical experience, with a robust background in model-based reinforcement learning and demonstrated expertise in one or more of the following areas:

• Planning algorithms.

• World models / learned dynamics surrogates.

• Reinforcement Learning and Deep Learning.

• Control Theory.

• Safe / constrained RL.

• A minimum of 2 years of research experience in academia or industry post-PhD graduation.

• Extensive research experience in the fields of {ModelBased, ModelFree, Safe}RL and Control Theory, with a particular focus on model-based approaches.

• Practical experience in building and evaluating agents using simulators (e.g., differentiable simulators or world models) and addressing the sim-to-real gap.

• Align with our company values: Collaboration, Transparency, Operational Excellence, Ownership, and Empathy.

🏝️ Benefits

• Dynamic, team-oriented environment where your contributions directly influence the company's trajectory.

• We operate as a fully remote organization.

• Competitive salary and meaningful equity options.

• Significant responsibilities and opportunities for professional growth.

• Comprehensive training programs including functional, customer immersion, and development training.

• Medical, dental, and vision insurance (specific benefits may vary by region).

• Unlimited paid time off, with a minimum requirement of 20 days annually.

• Paid parental leave (specific benefits may vary by region).

• Flexible stipends to support your workspace, well-being, and ongoing professional development.

• Company-provided MacBook.