AI Research Engineer: Adversarial Systems

Job Summary

Everyone else here builds the system. This role exists to prove its wrong.

Youd be measured on how much damage you do to our own work.

Why this work

India has been caught by surprise before. Not because the warning didnt exist, but because it existed somewhere in the system and nobody put it together in time. People died because of that gap. Were closing it.

Were building the intelligence system that helps India see the next attack coming before it happens, and helps it win the next war before the first shot is fired. Not with better sensors; India already collects enough. With the ability to actually use what it collects, at the speed the threat moves.

This doesnt get built by a foreign company, and it doesnt get built for a demo. It gets built by people who decided this mattered enough to build it here, for real, before its needed. If we do this right, the payoff is a warning that gets acted on in time, and a war thats already won in preparation before its fought at all.

A note on the obvious pitch

The tempting framing is self-play: agents run ten thousand scenarios overnight and find strategies no human would. Weve retired it. AlphaGo had a perfect simulator, a free reward signal, stationary rules, symmetric self-play. This domain has none of the four: its partially observable, the adversary adapts to you specifically, and outcomes are sometimes unobservable for years.

So the real question is what replaces the reward when the reward is unobservable, and what a self-play equilibrium means when the simulator is itself a hypothesis. If your instinct reading the AlphaGo line was that it breaks, thats the instinct this role runs on.

What youd work on

The deepest problem here: generating objections is trivial, models do it endlessly. Telling a critique that finds a real flaw from one thats merely well-formed is not, and as far as we can tell its unsolved.

Who this is for

An unusual role for an unusual person. Constitutionally sceptical, rigorous rather than reflexive about it. More excited by an experiment that disproves something than a demo that impresses someone. Able to argue for a flaw against people who dont want to hear it, including us.

Useful backgrounds: adversarial ML, security research, multi-agent systems, RL, game theory, forecasting and calibration, causal inference, experimental design. Rarer and valuable: real grounding in statistics or philosophy of science alongside the engineering. No defence background needed. No seniority needed.

To apply

Send a resume, plus the strongest argument you can make that were wrong.

Everything you need is above: an agentic reasoning system over messy multi-source data, no fine-tuning, self-hosted models, humans holding judgment at the end of every chain. Attack it. Find the assumption were leaning on hardest and break it.

Two pages, maximum. The best submission gets a conversation regardless of anything else in the file.

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