Hiring nowCausal inference, decisions and experimentation

Algorithmic Game Theory Researcher

Also called AI Game Theory Scientist

An Algorithmic Game Theory Researcher studies how agents respond to incentives inside computational systems. They design mechanisms and strategic models so the company's products hold up when users, models or counterparties act in their own interest.

What they do

They take a system where several parties make choices, such as a marketplace, an auction, a multi-agent setup or a reward scheme, and write down who the agents are, what they want and what they can do. Then they build a mechanism or strategic model. They test it with proof where the maths allows and with simulation where it does not. They run experiments to check that real behaviour matches the assumptions. They look for ways agents could game the design, and they write up the results so engineers and product teams can act on them.

What a strong hire delivers

In the first months, a strong hire produces a clear statement of agent assumptions for one live system. They deliver a mechanism or strategic model with a proof or a simulation that shows its incentive properties. They find at least one exploitable weakness and propose a fix. They leave the team with documentation and a simulation harness that others can rerun and extend.

How you can tell they are good

Ask them to take a system you run and state the agents, their goals and the assumptions behind any claim. A strong candidate is precise about what is proven and what is only simulated. They can explain where a result breaks if an assumption fails. Ask for a past mechanism or model and how they tested it. Worry if they cite equilibrium results without checking the setup fits.

Why it is hard to fill

Titles vary widely. The same work appears under mechanism design, economics research, multi-agent research or decision science. Few people combine formal theory with the practical habit of testing a design against real behaviour. Many strong theorists have never shipped anything, and many applied analysts lack the proof skills. Candidates are scarce, so we search across disciplines rather than on job titles.

Where to find them

Look to economics and computer science departments with a theory or market design focus, and to researchers in multi-agent systems. Others come from decision science, econometrics and experimentation teams at marketplaces, advertising businesses and platform companies. Some sit in the research groups of model providers, working on incentives and robustness. Strong applied candidates often hold statistics or causal inference backgrounds and have moved towards strategic modelling.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an Algorithmic Game Theory Researcher

  1. Describe a mechanism or strategic model you built. What agents did you assume, and how did you check the assumptions held?

    A strong answer names the agents, their goals and the information each had. It explains how assumptions were tested against data or experiments. Worry if assumptions are vague, untested or adopted from a textbook without checking the fit.

  2. When would you choose simulation over proof to establish an incentive property?

    Good answers recognise that proof suits tractable, clean settings and simulation suits messy or large ones. They discuss how to make simulations credible. Worry if they treat either as always sufficient or ignore the limits of simulation.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an Algorithmic Game Theory Researcher to study how agents respond to incentives in our systems. You will design mechanisms and strategic models, test them with proof and simulation, and help our teams build products that stay sound when participants act in their own interest.

What you will do

  • Define agents, goals and assumptions for our live and planned systems
  • Design mechanisms and strategic models for those systems
  • Prove incentive properties or test them with simulation
  • Search for exploitable weaknesses and propose fixes
  • Run experiments to check models against real behaviour
  • Document results clearly for engineering and product teams

What you will bring

  • Demonstrated mechanism design or strategic modelling work
  • Strong grounding in probability, statistics and game theory
  • Experience with experimental design and causal inference
  • Fluency in Python, and ideally R, for modelling and simulation
  • Clear writing that separates proven results from assumptions
  • Judgement about when proof or simulation is the right tool

Nice to have

  • Experience with multi-agent or marketplace systems
  • Familiarity with experiment platforms and causal analysis packages
  • Published or shared research on incentives

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Causal inferenceAdvancedSeparate causal claims from predictive associations
  • Experimental designAdvancedChoose comparisons that identify credible effects
  • Probability and statisticsWorkingQuantify variability and statistical evidence
  • Decision theory and game theoryWorkingAnalyse choices, incentives and strategic interactions

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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