ML Engineer

Artificial Societies operates at the frontier of predictive intelligence, using AI to simulate and predict human behaviour at scale. By generating and deploying instances of artificial intelligence that mirror humans, called agents, Artificial Societies simulates entire populations with unprecedented accuracy. Our partners work with us for many reasons: they leverage Artificial Societies to refine strategy in volatile geopolitical climates, cut new product innovation timelines from months to minutes, and deploy marketing campaigns that win in an era where consumers have never been harder to understand. We provide organisations with invaluable foresight, empowering them to anticipate outcomes and proactively make the right decisions at the right time, every time.

We're a small, dedicated, mission-driven team and we intend to stay that way. We believe the best work happens when exceptionally talented people are given ownership, trust, and the space to operate without bureaucratic friction. We work with urgency and intellectual honesty and expect new team members to match our velocity. We seek individuals who thrive at the frontier, who push beyond conventional limits, who bring curiosity and conviction in equal measure, and who want their work to have demonstrable impact in the world. If you're energised by the idea of a small team doing things that feel impossible, let's build together.

The problem

About the role

What you'll do

• Take research to production. Own the path from a working prototype to a deployed capability our clients depend on.
• Post-train and distil models. Fine-tuning, RL, and distillation to make our simulations nuanced and accurate.
• Own inference at scale. Serve simulations of thousands of individuals efficiently and reliably.
• Build the data engineering. Design the pipelines that turn messy real-world data into simulated populations at scale.
• Simplify aggressively. Reason about which parts of a complex method actually deliver the performance, and delete everything else.

What we're looking for

• At least 2+ years shipping ML in a startup environment, ideally as an AI or ML engineer at an early-stage company where you owned things end-to-end. You've put models in front of real users, not just in a notebook.
• Deep learning, not classical ML. Hands-on with pre-training or post-training language models - RL, fine-tuning, distillation, dataset and reward design.
• Strong Python engineering experience. You can write clean and well-structured Python code that other people can maintain.
• Comfortable with hard concepts. You reason fluently about statistics, probability, and have intuitions around high-dimensional spaces.
• Fast, with a high bar. You can ship quickly, and you go deep to understand the problem and the solution.
• Deeply interested in what we're doing. Simulating how people form opinions is a social science problem as much as an ML one, and the best people here find that fascinating rather than incidental.
• Ownership mentality. You don't wait for a ticket. You see what needs to happen, and you make it happen end-to-end.

Nice to have

• You've distilled a frontier model into a small open-weights one and shipped it to production to cut cost or latency.
• Experience with distributed training, inference optimisation, or scaling ML systems.
• Background in behavioural science, computational social science, psychometrics, statistics, or a related empirical field.
• Publications or open-source work in LLM simulation, evaluation, calibration, or human-behaviour modelling.
• Familiarity with TypeScript (our product stack) to collaborate smoothly with product engineers.

Logistics

• Location: London, in-office. We build better together, and this role is no exception.
• Salary: £100,000 - £180,000 depending on experience.
• Equity: Up to 0.20%.

Why join now