AI Engineer, Electric Twin
£80-120k
3 days a week in office
Building synthetic audiences that accurately model how humans think and behave
Job no longer available
Building synthetic audiences that accurately model how humans think and behave
21-100 employees
Job no longer available
£80-120k
3 days a week in office
21-100 employees
Company mission
To improve our understanding of the world.
Role
Who you are
- 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
- 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
- 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
What the job involves
- 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
- 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
- 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
Salary benchmarks
Our take
Electric Twin is a company utilising large language models to simulate human behaviour. Through its LLM models, users can create synthetic populations that represent their target markets or research demographics in order to investigate outcomes in near real-time and make more informed decisions.
The company supplies a range of investigative tools; from qualitative polling and quantitative analysis, to simulated focus groups or even one-on-one discussions. It also enables ideas to be continuously refined through strong feedback, ensuring that strategy can always be backed by the latest data.
With such a unique proposition, Electric Twin stands out as one to watch in the rapidly developing generative AI sector. It is currently working to build out its team and, if its models are able to deliver the value suggested, there will surely be no shortage of success in its future.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Company benefits
- We have an excellent health insurance package for employees with BUPA.
- All full time employees are enrolled onto our options scheme at the end of their probation period, which will be explained to new joiners in detail.
- We offer Unlimited Time Off and believe it is important for you to take sufficient rest through the year.
- Electric Twin has a generous pension scheme where we will match your individual contributions within a window of 4% - 8% under a net pay structure.
- Cycle to Work Scheme.
- We want to make sure we are providing you with what what you need to excel in your role. For IT we will provide a laptop and any accessories that you might want (e.g. monitor, keyboard etc).
- In an effort to stay on top of current trends and developments, we offer £1,000 annual L&D budget for you to use as you see fit.
Company HQ
Soho, London, UK
Leadership
Alex Cooper
(Co-Founder)Former COO at UNDO, Director of Mass Testing for the Department of Health and Social Care, and Advisor for 10 Downing Street. Was also an Officer in the British Army for 19 years.
Ben Warner
(Co-Founder)Former Chief advisor on Digital and Data to the Prime Minster with the UK Civil Service. Worked in Data Science at Faculty and was an EPSRC Research Fellow at UCL.
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