Founding Engineer - Research

Job description

About

Governments and policymakers make decisions that shape millions of lives, but they still operate with slow research cycles, fragmented evidence, and expensive consultant reports that often arrive only after programs have launched. We believe AI can help public institutions make better decisions: faster, more evidence-based, more responsive, and more accountable to the people they serve.

Our platform helps public institutions synthesize evidence, simulate how policy choices may affect people, budgets, and outcomes before rollout, monitor real-world results, and continuously improve decisions over time.

We closed a $5.7M seed round earlier this year, led by Relentless, with participation from a16z speedrun, Liquid 2, Operator Collective, Entourage, Station F, Plug and Play and others. We are now scaling across the US and Europe and hiring founding team members to help build a new category at the intersection of AI, simulation, public policy, and institutional decision-making.

Job Description

The role

A large part of our work involves extending the frontier of simulations and LLMs in the fields of policy and social sciences. You’ll be leading these efforts to benchmark various models on problems across the policy spectrum. This is a hybrid role: part applied researcher, part production engineer.

Day to day, you'll:

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Move fluidly between research and production, collating research, prototyping ideas and turning it into useful product.

· 
Benchmark simulations on new policy problems across fields

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Build specialised research agents for social and economic policy

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Own the AI/ML layer end-to-end: data pipelines, model selection, inference infrastructure, monitoring, and the feedback loops that make it better over time. Sit in on user research and customer calls so your work is grounded in real workflows, not assumptions.

Preferred Experience

You're a fit if you

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2–3 years building ML systems in production — at a startup, research lab, or product team OR 2-3 years research experience in ML or applied stats.

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Strong Python and a solid grasp of modern ML tooling, experience fine-tuning models.

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A track record of going from research idea to working system, with a clear point of view on what's hype and what's real.

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Comfort with ambiguity, sparse specs, and the gap between "the model can do this" and "users can rely on this."

· 
Genuine interest in public sector problems.

Nice to have

· 
Exposure to government, policy, or public-sector consulting

· 
Published research, open-source contributions, or strong writing about applied ML, especially in regulated or sociological domains.

· 
Experience in econometrics, causal inference

Recruitment Process

Our process is lightweight and designed to help both sides assess fit quickly. It typically includes an initial conversation with a founder, a deeper role-specific interview, and a short practical exercise or case discussion. Finalists meet all founders before we make an offer.

Additional Information

·  Contract Type: Full-Time
·  Location: Paris, London
·  Possible partial remote