Research Scientist/Engineer - General Decision & Control Agent
Adecco · London · posted 32 days ago
Going rate £41,500UK median £43,384
Home Office going rates from
Occupation
2119Natural and social science professionals n.e.c.
Going rate for this occupation: £41,500 · UK median pay £43,384
Home Office going rates from
Where this salary sits
- UK pay for this occupation
- This role£100,000 to £180,000stated · above the range ONS published
- Going rate£41,500
- UK median£43,384
View these figures as a table
| Percentile | Pay |
|---|---|
| 10th | £28,682 |
| 25th | £34,431 |
| 50th | £43,384 |
| 75th | £53,804 |
| Going rate | £41,500 |
| UK median | £43,384 |
Sponsorship
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Research Scientist/Engineer - Agent Systems & Reinforcement Learning
Location: London Salary: £ per annum + permanent benefits + bonus Job Type: Permanent, Full-Time, On-site
About the Opportunity
We are partnering with a leading AI research organisation focused on developing sustainable, generalisable and evolvable Agent systems that represent the next frontier of artificial intelligence. This team is exploring how autonomous AI agents can operate effectively across complex environments, continuously learn from experience, and improve their capabilities over extended periods of execution.
This is an exceptional opportunity to join a world-class research environment working at the intersection of Agents, Large Language Models, Reinforcement Learning and Autonomous Systems , contributing to cutting-edge research that could play a significant role in advancing the path towards Artificial General Intelligence (AGI).
You will work alongside internationally recognised researchers and engineers, with access to significant computational resources and the freedom to investigate ambitious research challenges while helping translate breakthrough ideas into practical AI systems.
The Role
As a key member of the research team, you will contribute to the design and development of next-generation Agent systems, focusing on long-term reasoning, memory, self-improvement and reinforcement learning-driven optimisation.
Key Responsibilities
Agent Memory & Long-Term Reasoning
Design and develop advanced Agent memory architectures capable of supporting ultra-long context processing. Research techniques to mitigate memory degradation in long-running Agent environments. Improve information retrieval, storage and utilisation mechanisms to enhance long-term planning and decision-making. Explore scalable approaches for persistent memory systems across complex task environments.
Agent Self-Evolution & Autonomous Learning
Develop self-evolving Agent capabilities that enable continuous improvement through experience. Research unified Agent representations and optimisation frameworks to support autonomous adaptation. Build systems that facilitate iterative self-improvement and long-term learning. Contribute to the development of Agent Harness frameworks that enable scalable evolution of autonomous agents.
Agentic Reinforcement Learning
Investigate advanced reinforcement learning methodologies for Agent optimisation. Develop both parametric and non-parametric RL approaches to improve Agent performance. Build collaborative update pipelines connecting Agent policy models and Agent execution frameworks. Evaluate and improve learning efficiency across diverse environments and task domains.
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