Electric Twin
AI Engineer
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The Role As an AI Engineer you will be part of the wider technical team but positioned within Science.You’ll research and build the systems that bring our AI agents to life and you will work with engineering to scale the infrastructure that powers our LLM-driven synthetic populations. You'll design agent cognitive architectures, implement context engineering and memory systems, while ensuring these AI systems can operate reliably at scale in production environments. This role balances AI agent research with backend engineering. It is ideal for research engineers who want to work directly with large language models to create realistic behavioural simulations, while building the robust infrastructure needed to deploy them in enterprise settings. \n
What You'll Do There are three main areas that members of the science team focus on:
Research, Modelling and Experimentation: You will design and run systematic experiments to evaluate synthetic agent behaviour, test hypotheses about behavioural patterns, and iterate on model architectures based on empirical results and validation against real-world data.
LLM Product Engineering: You will build sophisticated prompting strategies, behavioural frameworks, and decision-making systems that enable agents to exhibit realistic human-like behaviour across diverse scenarios and demographics. You will combine this with client interactions to ensure product viability for the end customer.
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Architecture & Development: Design and implement the cognitive systems that give AI agents consistent personalities, memory, and reasoning capabilities, using advanced LLM techniques like chain-of-thought prompting, RAG systems, and agentic tool use.
Who You Are Essential Qualifications Bachelor's or Master's degree in CS, Math, Physics, AI, or related technical field. Strong foundation in both AI/ML concepts and backend engineering principles. Experience working in fast-paced environments where requirements evolve rapidly. Over 7 years of experience with at least 1 year working hands-on with large language models to solve complex problems. Technical Skills Research: Proof of fast iteration and experimentation in order to validate model performance and outputs. Exposure to research-driven product development or academic AI research is desirable. Knowledge of fine-tuning workflows, model optimisation, and experiment tracking. Understanding of statistical validation and data quality assessment. Experience with frameworks for building AI / LLM applications (e.g. PyTorch, Hugging Face Transformers, LangChain). LLM & Agent Development: Hands-on experience building applications with large language models, implementing advanced prompting techniques, RAG systems, and agentic workflows. Experience with multi-agent systems, simulation frameworks, or agent-based modelling. Backend Engineering: Proficient in Python and backend frameworks (e.g. FastAPI, Django, Flask); understanding of distributed systems and scalable architectures. Personal Attributes Strong ownership mentality—you see projects through from design to deployment. Pragmatic problem-solver who balances technical elegance with business needs. Clear communicator who can explain complex technical decisions to non-technical stakeholders. Thrives in ambiguity and adapts quickly as product requirements evolve. Passionate about building infrastructure that enables innovative AI applications. Intellectually honest—willing to question prevailing approaches and advocate for better solutions when evidence supports it. Collaborative mindset—debates ideas vigorously while respecting other perspectives.
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\n £80,000 - £120,000 a year \n What We Offer We offer a competitive package designed to support you properly, not just on paper. Competitive salary. Meaningful equity in a high-potential seed-stage company. Unlimited leave. Take the time you need. Generous matched pension contributions. Private healthcare. Cycle to work scheme. Direct access to and collaboration with world-class founders. Hybrid working from our London office (4 days in office a week). Flexible working around life commitments. We value outcomes over presenteeism.
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