Senior AI Research Scientist (Model-based RL)

Accountabilities: Design, implement, and evaluate model-based reinforcement learning agents, including planning-based controllers such as MPC and MPPI, as well as the software prototypes required for deployment in real industrial control systems.

Develop learned dynamics models and world models capable of generalizing across different systems, including training approaches such as pretraining, curriculum learning, active learning, adversarial learning, and fine-tuning.

Research and apply advanced methods in safe reinforcement learning, constrained control, scenario planning, Bayesian reinforcement learning, and related areas to ensure reliable and secure AI agent deployment.

Translate research discoveries into practical outcomes by developing production-ready solutions and leading the rollout of research initiatives or large-scale projects.

Communicate research findings, technical developments, and project results clearly through written documentation, presentations, and internal or external discussions.

Collaborate with research teams, engineers, and external partners to transform innovative AI concepts into impactful industrial applications.

Mentor and guide Research Engineers by helping them apply advanced AI research methodologies to complex industrial challenges.

Independently define new research directions and contribute to the long-term evolution of intelligent control technologies.

Requirements:

PhD in machine learning, control systems, computer science, or a related technical field, or equivalent practical experience with strong expertise in model-based reinforcement learning.

At least 2 years of research experience in academia or industry after completing a PhD.

Deep knowledge and hands-on experience in areas such as model-based reinforcement learning, model-free reinforcement learning, safe reinforcement learning, planning algorithms, world models, learned dynamics models, deep learning, or control theory.

Proven experience building and evaluating AI agents using simulators, including experience addressing the challenges of simulation-to-real-world transfer.

Strong programming skills in Python and experience with machine learning frameworks such as PyTorch and scientific computing libraries such as SciPy.

Experience working with scalable experimentation environments and infrastructure such as distributed computing, Ray, Kubernetes, Docker, or cloud platforms like GCP.

Strong research background demonstrated through publications or contributions in reinforcement learning, control systems, artificial intelligence, or related fields.

Ability to collaborate effectively in a remote, international environment while demonstrating ownership, transparency, empathy, operational excellence, and strong teamwork.

Passion for applying AI research to industrial systems and improving efficiency, sustainability, and resource utilization.

Benefits:

Competitive base salary ranging from £87,681 to £165,379 , depending on location tier, experience, qualifications, and other relevant factors.

Eligibility for meaningful equity participation.

Fully remote work environment with flexibility across multiple time zones.

Medical, dental, and vision insurance, with benefits varying depending on location.

Unlimited paid time off with a required minimum of 20 days per year.

Paid parental leave, depending on regional policies.

Flexible stipends supporting workspace setup, personal well-being, and professional development.

Company-provided MacBook.

Opportunities for significant ownership, career growth, and professional development in a fast-paced AI-focused environment.

Access to training programs covering technical skills, product knowledge, customer immersion, and professional growth.

Remote-first culture built around documentation, asynchronous collaboration, regular team communication, and virtual team-building activities.

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