Research Engineer

What you'll do

• Productionize audience generation, world modeling, and response prediction, including reusable simulation abstractions.
• Build evaluation and accuracy infrastructure, and make simulations fast and cost-effective at scale.
• Develop deployment workflows and the analysis and publication layers that turn agent output into customer-ready artifacts.

What they ask for

• Deep expertise in ML and AI, including meaningful prior work with Large Language Models in production systems.
• Experience designing and running rigorous ML experiments from hypothesis through evaluation and deployment.
• 3+ years of hands-on experience building ML/AI systems (research or applied).
• Comfort with the full model lifecycle and experience working cross-functionally to ship ML-powered features.

Nice to have

• Publications at top ML venues (NeurIPS, ICML, ICLR, ACL).
• Experience with multi-agent systems, probabilistic or causal methods, simulation, or hybrid LLM and classical architectures.

Tools

Large Language Models, LLMs, Monte Carlo methods

AI focus

Builds AI simulation systems that model audiences and worlds, predict human responses, evaluate accuracy, and deliver decision-ready outputs; includes production LLM and hybrid LLM/classical architectures.