Lead Research Engineer (Multi-Agent Systems)

London (Hybrid)

£200-350k base + equity

About Us

We’re an early-stage, venture-backed AI startup on a mission to push the boundaries of agentic AI. Our core focus is onmulti-agent systemsand leveragingLarge Language Models (LLMs)to pioneer novel architectures and algorithms that enable intelligent agents to collaborate, learn, and adapt in complex environments. You’ll be joining a fast-paced, passionate team dedicated to transforming the future of AI research and its applications.

The Role

We are seeking aLead Research Engineerto spearhead the development of our multi-agent system initiatives, including the integration ofLLMsinto agentic architectures. You’ll be both hands-on and strategic—responsible for overseeing the technical roadmap, guiding a small but growing team of engineers, and contributing directly to the design and implementation of cutting-edge AI solutions. This role requires a proven track record in building commercial multi-agent systems (4–6 years of experience), combined with strong leadership and mentorship capabilities.

Key Responsibilities

• Technical Leadership: Lead the end-to-end development of multi-agent system solutions, incorporatingLLMsfor advanced language understanding, from proof-of-concept to production-ready deployments.
• Research & Development: Drive research on state-of-the-art approaches in agentic AI, reinforcement learning,large language models, and distributed systems; translate findings into feasible products.
• Systems Architecture: Architect scalable, modular, and robust system designs that can handle the complexities of multi-agent collaboration and LLM integration.
• Mentorship & Team Building: Coach junior engineers and research scientists, foster a culture of innovation and continuous improvement, and grow the R&D team.
• Collaboration: Work closely with product managers, data scientists, and stakeholders to align technical solutions with business objectives.
• Innovation: Evaluate emerging technologies and methodologies—especially around LLMs—ensuring our solutions remain at the forefront of AI research.

Qualifications

• Experience: 4–6 years of commercial experience developing and deployingmulti-agent systems(e.g., reinforcement learning agents, distributed AI), with exposure toLLM-basedsolutions a big plus.
• Technical Expertise: Proficiency in one or more programming languages commonly used in AI development (Python, C++, etc.) and familiarity with relevant ML frameworks (TensorFlow, PyTorch, or similar).
• Research Acumen: Strong understanding of advanced AI topics such as distributed learning, game theory, agent-based modeling, reinforcement learning, andLLMintegration.
• Architectural Know-How: Demonstrated experience in designing large-scale, distributed systems with an emphasis on performance and reliability.
• Leadership Skills: Proven ability to lead technical teams, manage projects, and mentor junior engineers.
• Education: Bachelor’s or Master’s degree in Computer Science, AI, or a related field (PhD a plus, but not required).