Lead Research Engineer (Agentic AI)

ziprecruiter
London
2 months ago
Applications closed

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Job Description

If you are considering sending an application, make sure to hit the apply button below after reading through the entire description.Lead Research Engineer (Multi-Agent Systems)London (Hybrid)£200-350k base + equityAbout UsWe’re an early-stage, venture-backed AI startup on a mission to push the boundaries of agentic AI. Our core focus is on

multi-agent systems

and leveraging

Large 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 RoleWe are seeking a

Lead Research Engineer

to spearhead the development of our multi-agent system initiatives, including the integration of

LLMs

into 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 ResponsibilitiesTechnical Leadership : Lead the end-to-end development of multi-agent system solutions, incorporating

LLMs

for advanced 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 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.QualificationsExperience : 4–6 years of commercial experience developing and deploying

multi-agent systems

(e.g., reinforcement learning agents, distributed AI), with exposure to

LLM-based

solutions a big plus.Technical Expertise : Proficiency in one or more programming 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, and

LLM

integration.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).

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