hybrid
AI Research Engineer
AI Research Engineer
The AI Research Engineer will join a well-funded early-stage startup to build advanced AI systems for modelling and simulating complex real-world behavior. This role involves deep research expertise at the intersection of large language models, agent systems, and scalable backend infrastructure, focusing on agent cognition, memory, reasoning, and orchestration.
About the role
About the Role
This early-stage, well-funded startup is building advanced AI systems that model and simulate complex real-world behaviour at scale. Their platform enables organisations to test decisions, scenarios and strategies using large-scale AI-driven simulations, dramatically reducing time-to-insight compared to traditional approaches. Following strong early traction and funding injection, they're looking for 2x AI Engineers with deep research expertise to join a small, highly technical team working at the intersection of large language models, agent systems and scalable backend infrastructure.
Responsibilities
- Formulating research questions about human / agent behaviour
- Designing experiments on synthetic populations
- Validating model behaviour against real-world data
- Deciding what architectures and methods should exist, not just implementing them
Requirements
Must have requirements:
- PhD / MSc or substantial research experience in AI, ML, CS, Cognitive Science, Physics, Mathematics, or a related field
- Demonstrated ability to conduct independent, hypothesis-driven research
- Strong grounding in experimental design, statistical validation, quantitative evaluation
- Strong software engineering fundamentals in Python and backend frameworks like FastAPI, Flask, Django
- Hands-on experience working with ML / AI models (LLMs, NLP, simulation, or related areas)
- Comfortable working in ambiguity, where the right question is often unclear at the start
Bonus points for:
- Familiarity with fine-tuning workflows, model optimisation and experiment tracking
- Experience with multi-agent systems, simulations or agent-based modelling
- Experience building workflows/agents on top of existing models
- Experience with relational databases and vector search / embedding systems
- Knowledge of cloud infrastructure, containerisation and deploying ML systems to production
- Experience working in fast-moving environments with evolving requirements