Applied Research Engineer

Founding Applied Research Engineer – Simulation & Evaluation

New York City (on-site) | $300k–$500k base + significant founding equity

Can a machine tell whether a creative idea will become iconic?

My client is a well-funded applied AI research lab building systems capable of superhuman creativity. Most labs are working on models of the physical world. This team is working on taste, emotion and creative judgement. The near-term goal is world-class ad campaigns, and the long-term vision runs all the way to feature-length film.

The team is small and talent-dense, made up of engineers, researchers and artists. Its founders combine elite technical backgrounds with serious creative pedigree.

The role

The lab’s creative models surface exceptional concepts. You’ll build what comes next: the system that tests those ideas against the world they’re meant to enter, including audiences, culture, brands and real behaviour. You’ll then turn the results into feedback the models can learn from. The aim isn’t to reward safe ideas. It’s to find out whether something extraordinary can actually land.

What you’ll do

• Design and build multi-agent simulation systems that orchestrate thousands of LLM agents, each with its own profile, memory and state
• Model how ideas interact with audiences, culture, brands and market signals
• Tackle the hard problems of synthetic audiences: positivity bias, low variance between agents, prompt sensitivity and drift into easy consensus. You’ll calibrate everything against real human responses
• Build experimentation, calibration, uncertainty and validation pipelines tied to real-world outcomes
• Serve open-weight models on in-house clusters at scale, without changing simulation fidelity

What we’re looking for

• Hands-on experience in at least one of: simulation, synthetic populations, forecasting, causal inference, recommendation/ranking, survey or behavioural data, or computational social science
• You’ve built LLM multi-agent systems in production or research (roles, memory, message passing, turn-taking)
• You’ve deployed open-weight models and know the serving stack well: KV/prefix caching, quantisation (AWQ, GPTQ, FP8) and multi-LoRA serving. You can profile and fix a bottleneck
• Strong ML engineering, software fundamentals and statistical instincts
• A research mindset: you can turn an ambiguous question into a testable system and know when the evidence is weak

Strong plus: founding or early-stage experience, a track record of taking research through to product, and taste or fluency in a creative field such as advertising, film, design, writing, music, art or games.

Why join

• A founding seat on a problem almost nobody else is tackling, with real influence over technical direction and vision
• Top-of-market compensation, with flexible base/equity packages
• A fast-moving process with a high-calibre team

Worth knowing up front: this is an intense, in-person environment, with at least 4 days on-site and a 6-day week. It’s built for people who want to go all-in on something ambitious.