Research Engineer

About Electric Twin

Electric Twin is building the future of strategic decision-making. We use large language models (LLMs) to simulate human behaviour, allowing our clients to create science-based synthetic populations that mirror their target markets and research demographics.

Our platform provides a suite of investigative tools—from qualitative polling and quantitative analysis to simulated focus groups and one-on-one discussions. This enables leaders in business and government to investigate outcomes in near real-time, continuously refine ideas with powerful feedback, and make critical decisions with confidence.

With a unique proposition in the rapidly developing generative AI sector, Electric Twin is poised for significant growth. We are backed by world-class leadership, including:

• Alex Cooper (Co-founder): Former COO at UNDO, Director of Mass Testing for the UK's Department of Health, and Advisor for 10 Downing Street.
• Ben Warner (Co-founder): Former Chief Advisor on Digital and Data to the UK Prime Minister, Data Scientist at Faculty, and EPSRC Research Fellow at UCL.

We build science-based synthetic populations and the tools to interact with them. The world's leading companies and governments use Electric Twin to make better decisions. Now, we are looking for brilliant minds to join us on this mission.

The Role

As a Research Engineer at Electric Twin, you will focus on one of the most exciting and challenging applications of AI: modeling human behaviour. Your primary role is to pioneer the application of existing LLMs to create believable, consistent, and scientifically-grounded AI agents. You will design the cognitive architecture of our synthetic agents, figure out how to evaluate their behaviour in ambiguous scenarios, and develop the experimental methods to validate our simulations.

You will be at the forefront of an emerging field, tackling questions like: How can we use an LLM to give an agent a persistent memory? How do we ensure a population of agents behaves in a way that is statistically representative of a real demographic? How do we measure and validate "realism" when there is no simple ground truth?
This is a deeply interdisciplinary role that combines creative AI application, rigorous experimental design, and insights from computational social science to bring our synthetic populations to life.

Key Responsibilities

• Design AI Agents: Develop sophisticated prompting strategies, retrieval-augmented generation (RAG) systems, and tool-use frameworks to imbue AI agents with consistent personas, memories, and reasoning capabilities.
• Develop Experimental Methods: Design and execute experiments to test and validate the behavioural outputs of LLM-powered agents against real-world data and established social science principles.
• Create Novel Evaluation Frameworks: Build and implement robust methods for measuring the quality and realism of our simulations, especially on ambiguous tasks where traditional accuracy metrics don't apply.
• Build the Simulation Platform: Contribute to the core architecture that allows us to run, observe, and analyse complex simulations with thousands of interacting AI agents.
• Translate Needs into Research: Work closely with product and commercial teams to understand client challenges and translate them into focused research questions and technical solutions.
• Stay at the Forefront: Research and apply the latest techniques in agentic AI, multi-agent systems, and LLM evaluation to continuously advance our platform's capabilities.

Requirements

We are looking for a creative and rigorous engineer who is passionate about using AI as a tool to understand the human world.
Essential Skills & Experience:

• MS or PhD in Computer Science, Machine Learning, Computational Social Science, or a related quantitative field, or equivalent practical experience.
• Deep experience in applying large language models (LLMs) to solve complex, open-ended problems.
• Strong proficiency in Python and common ML libraries/frameworks (e.g., PyTorch, JAX, TensorFlow, Hugging Face).
• A strong scientific mindset geared towards rigorous experimentation, coupled with the engineering discipline to build the reliable and scalable systems needed to test complex hypotheses.
• A keen interest and/or background in the principles of human behaviour, cognitive science, psychology, or social dynamics.
• Excellent communication skills, with the ability to articulate complex technical and experimental concepts clearly.

Bonus Points (Advantageous, but not required):

• Experience or strong interest in multi-agent systems, agent-based modeling (ABM), or game theory.
• Hands-on experience with advanced LLM application techniques like RAG, chain-of-thought, and agentic tool use.
• Experience designing and conducting experiments in a social science or human-computer interaction (HCI) context.
• A track record of publications in relevant AI or interdisciplinary conferences.
• Experience deploying ML-driven applications into production environments.

Benefits

• Define a Category: Shape the core application of a company pioneering a new frontier in AI and strategic intelligence.
• Solve Fascinating Problems: Work on intellectually stimulating challenges that blend creative AI application with deep insights into human behaviour.
• World-Class Team: Join a small, high-caliber team with proven leadership from the highest levels of technology, government, and business.
• High Impact: In our flat, collaborative environment, your work will have an immediate and tangible impact on our product and our clients' success.
• Growth Opportunity: As an early member of the team, you'll have significant opportunities for personal and professional development as the company grows.