Job description

ABOUT AARU Aaru operates at the frontier of predictive intelligence, using AI to simulate and predict human behavior at scale. By generating and deploying instances of artificial intelligence that mirror humans, called agents, Aaru simulates entire populations with unprecedented accuracy. Our partners use Aaru to refine strategic positioning, identify and understand high-value audiences, validate concepts and messaging before launch, optimize pricing decisions, and build a continuously richer understanding of their customers through simulation. We provide organizations with invaluable foresight, empowering them to anticipate outcomes and proactively make the right decisions at the right time, every time. We're a small, dedicated, mission-driven team and we intend to stay that way. We believe the best work happens when exceptionally talented people are given ownership, trust and the space to operate without bureaucratic friction. We work with urgency and intellectual honesty and expect new team members to match our velocity. We seek individuals who thrive at the frontier, who push beyond conventional limits, who bring curiosity and conviction in equal measure, and who want their work to have demonstrable impact in the world. If you're energized by the idea of a small team doing things that feel impossible, let’s build together. ABOUT THE ROLE Research Engineers own the path from research idea to production system. Researchers prove out how to synthesize an audience, model a world, predict responses, score accuracy, or ship a result; you turn those ideas into robust, reusable, fast code — the objects, contracts, evaluations, and workflows that produce the product. RESPONSIBILITIES - Productionize the core simulation loop — audience generation, world modeling, response prediction. - Design the reusable abstractions that the product is made of: agent and population objects, simulation contracts and interfaces, evaluation harnesses, etc. - Build and own evaluation and accuracy infrastructure. - Make simulations fast and cheap enough to run at scale. - Partner closely with Simulation Research to harden hybrid LLM + classical architectures. - Build the workflows and tooling that let deployment and forward-deployed teams stand up new client simulations. - Create the calculation, analysis, and publication layers that turn raw agent output into the decision-ready artifacts customers see. - Hold the line on engineering quality across a fast-moving codebase. YOU MAY BE A FIT IF - You have deep expertise in ML and AI, with meaningful prior work using Large Language Models in production systems. - You've designed and run rigorous ML experiments, moving from hypothesis to evaluation to deployment. - You have 3+ years of hands-on experience building ML/AI systems (research or applied). - You're comfortable working across the full model lifecycle: data collection, training, evaluation, and production integration. - You have experience working cross-functionally with engineering and product teams to ship ML-powered features. - You're deeply curious and motivated to push the boundaries of what simulation technology can do. STRONG CANDIDATES MAY ALSO - Have publications at top ML venues (NeurIPS, ICML, ICLR, ACL) - Have experience building or fine-tuning multi-agent systems - Have worked on probabilistic modeling, Bayesian inference, or causal reasoning - Have familiarity with our stack or demonstrate the ability to learn unfamiliar technologies quickly - Have direct experience with simulation, agent-based modeling, Monte Carlo methods, or other modeling-and-prediction systems. - Have experience with hybrid architectures that combine LLMs with classical statistical or ML methods. - Have background in a quantitative or data-heavy domains — quant finance, computational social science, ad measurement, forecasting, statistics, or psychometrics. - A research sensibility: comfort reading papers, prototyping alongside researchers, and contributing to or co-authoring published work. LOCATION This role is based in New York City. Aaru is an in-person company, working 5 days a week in office. Candidates are expected to be located within the New York City metropolitan area or open to relocation. BENEFITS At Aaru, we take care of our people. In addition to a competitive base salary and equity participation, we offer comprehensive medical, vision, and dental coverage, visa sponsorship and relocation support, and various other benefits and perks.

Requirements

Must-haves

Productionize the core simulation loop — audience generation, world modeling, response prediction.
Design the reusable abstractions that the product is made of: agent and population objects, simulation contracts and interfaces, evaluation harnesses, etc.
Build and own evaluation and accuracy infrastructure.
Make simulations fast and cheap enough to run at scale.
Partner closely with Simulation Research to harden hybrid LLM + classical architectures.
Build the workflows and tooling that let deployment and forward-deployed teams stand up new client simulations.
Create the calculation, analysis, and publication layers that turn raw agent output into the decision-ready artifacts customers see.
Hold the line on engineering quality across a fast-moving codebase.
You have deep expertise in ML and AI, with meaningful prior work using Large Language Models in production systems.
You've designed and run rigorous ML experiments, moving from hypothesis to evaluation to deployment.
You have 3+ years of hands-on experience building ML/AI systems (research or applied).
You're comfortable working across the full model lifecycle: data collection, training, evaluation, and production integration.
You have experience working cross-functionally with engineering and product teams to ship ML-powered features.
You're deeply curious and motivated to push the boundaries of what simulation technology can do.
Have publications at top ML venues (NeurIPS, ICML, ICLR, ACL)
Have experience building or fine-tuning multi-agent systems
Have worked on probabilistic modeling, Bayesian inference, or causal reasoning
Have familiarity with our stack or demonstrate the ability to learn unfamiliar technologies quickly
Have direct experience with simulation, agent-based modeling, Monte Carlo methods, or other modeling-and-prediction systems.
Have experience with hybrid architectures that combine LLMs with classical statistical or ML methods.
Have background in a quantitative or data-heavy domains — quant finance, computational social science, ad measurement, forecasting, statistics, or psychometrics.
A research sensibility: comfort reading papers, prototyping alongside researchers, and contributing to or co-authoring published work.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmml

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision