Product Engineer

About the company

Aaru provides near-instant customer research by using AI agents to simulate human behavior and predict market responses.

About this role

ABOUT AARU Aaru builds simulations of human behavior. Each simulation contains a population of AI agents, each representing a person who could plausibly exist in the real world and capable of making decisions within a modeled environment. Companies and institutions use these simulations to test consequential choices before committing—from product launches and pricing decisions to strategic communications and policy changes. Building a useful simulation requires more than generating plausible text. Populations must represent real people and groups; predictions must be calibrated; simulations must remain coherent as conditions change; and the product must make the resulting evidence legible enough to support real decisions. We are a small, in-person team in New York. We work with urgency, high ownership, and intellectual honesty. We expect people to surface inconvenient evidence, change their minds quickly, and carry important work all the way to a result. ABOUT PRODUCT ENGINEERING Product Engineering turns Aaru's simulation capabilities into products that customers can use independently and repeatedly. The team builds on Aaru's shared platform and simulation systems to create the workflows, interfaces, integrations, and decision-ready artifacts that make a technically sophisticated system feel clear and dependable. The work is organized around durable product domains rather than a queue of isolated features. Those domains may include simulation setup, question and experiment types, follow-up and continuous simulations, analysis and reporting, reusable customer templates, collaboration, and external integrations. This is not a thin frontend role. Product Engineers own user outcomes across the stack. A project may begin with observing a customer's decision process, continue through product and systems design, require new backend or orchestration primitives, involve careful evaluation of model behavior, and end with a measured production rollout. THE ROLE As a Product Engineer, you will take ambiguous, consequential user problems from first conversation to reliable product capability. You will work closely with Product, Design, Deployment, Platform Engineering, Simulation Engineering, and Research. You will be expected to understand both the user problem and the system details well enough to make good tradeoffs without handing ownership away at either boundary. You will prototype quickly, but you will not confuse a compelling demo with a finished product. The systems you ship must be understandable, observable, secure, maintainable, and robust to the variability of AI-generated behavior. When a shared primitive is missing, you will work through the platform boundary or help create it rather than building a fragile one-off around it. WHAT YOU WILL DO - Own product work end to end: understand the problem, define the smallest useful solution, design the system, implement it, roll it out, measure it, and support it in production. - Work directly with users and customer-facing teams to observe real decision workflows, identify recurring needs, and distinguish durable product opportunities from bespoke requests. - Build polished customer-facing experiences across frontend, backend, APIs, data models, workflow orchestration, permissions, integrations, and AI-driven interactions. - Translate capabilities from Population Research, Prediction Research, Evaluation Research, and Simulation Engineering into product experiences that customers can use without expert assistance. - Design product behavior around the uncertainty and variability of AI systems, including clear states, human review points, fallbacks, retries, provenance, and honest communication of confidence. - Define evaluation and launch criteria before shipping model-dependent features. Use offline evaluations, product signals, operational metrics, and qualitative feedback to determine whether a change is actually better. - Turn specific customer evidence into generalizable product primitives, templates, and workflows rather than accumulating one-off branches and configuration. - Work with Platform Engineering through clear interfaces, contribute missing primitives when appropriate, and avoid coupling product delivery to undocumented platform behavior. - Instrument adoption, task completion, quality, latency, cost, reliability, and failure modes so that product decisions are based on evidence rather than anecdotes. - Own the operational quality of what you ship, including production support, debugging, incident follow-up, migrations, and safe rollback paths. - Write clear technical designs, product notes, and launch documentation. Make scope, assumptions, dependencies, and unresolved risks legible to the rest of the company. - Raise the quality bar through thoughtful code review, testing, design critique, and improvements to the tools and patterns used by the broader engineering team. REPRESENTATIVE PROBLEMS You might work on problems such as: - Add a new question or allocation format that requires changes to the product interface, simulation contract, validation logic, analysis layer, and customer-facing output. - Build a continuous simulation product that ingests new information over time, updates relevant assumptions, and shows users what changed and why. - Create a follow-up workflow that lets a user interrogate a completed simulation without losing provenance, population state, or the distinction between observed and generated evidence. - Turn a customer's successful simulation setup into a reusable organization-specific template with sensible defaults, permissions, versioning, and audit history. - Build an agent-assisted setup experience that helps users specify a decision, identify missing context, and construct a valid simulation without hiding important assumptions. - Create decision-ready reports, presentations, or interactive artifacts that preserve uncertainty and trace each co