Researcher, Research Development, Research Scientist

About This Role

This Researcher position involves designing, building, and improving simulation systems that generate high-fidelity synthetic agents from real-world profiles. The Research team is responsible for the company's core simulation capabilities, which enable customers to test decisions against realistic populations before committing to them. The role suits an individual who thrives at the frontier, pushing beyond conventional limits with curiosity and conviction.

Responsibilities

• Design and advance methods for synthesizing realistic agents from structured and unstructured profile data
• Build and evaluate simulation pipelines that produce population-level distributional accuracy across demographic, attitudinal, and behavioral dimensions
• Develop techniques for grounding agent behavior in real-world data sources such as surveys, behavioral traces, psychometric instruments, and domain-specific datasets
• Design hybrid architectures that combine large language models with classical methods from computational social science, psychometrics, and survey methodology
• Build rigorous evaluation frameworks that measure simulation quality against known ground-truth distributions (e.g., GSS, census, proprietary panel data)
• Identify and close gaps between simulated and observed population behavior through iterative experimentation

Requirements

• Deep experience with synthetic data generation, agent-based modeling, computational social science, or population simulation
• Interest in using LLMs as a simulation substrate, not just for text generation, but for producing structured behavioral outputs that hold up under statistical scrutiny
• Ability to think carefully about what it means for a simulation to be "accurate" and articulate the difference between individual-level plausibility and distributional fidelity
• PhD in ML, computational social science, cognitive science, statistics, psychometrics, or a quantitative field, or equivalent depth from industry research
• Ability to communicate clearly about what's working and what isn't, including when results are null or ambiguous
• Ability to move fast and produce work at high velocity without sacrificing rigor

Nice to have:

• Built systems that generate or evaluate synthetic populations at scale
• Experience with survey methodology, psychometrics, or demographic modeling
• Published work on agent-based simulation, synthetic data, or LLM-based behavioral modeling
• Worked in a high-velocity research environment where shipping matters as much as rigor

Benefits

• Comprehensive medical, vision, and dental coverage
• Visa sponsorship and relocation support
• Various other benefits and perks