Product Engineer

Aaru•New York, NY

78d•Onsite

About The Position

Aaru builds simulations of human behavior, where each simulation contains a population of AI agents representing real-world individuals making decisions within a modeled environment. Companies use these simulations to test consequential choices before committing to product launches, pricing decisions, strategic communications, and policy changes. Building a useful simulation requires more than generating plausible text; populations must represent real people, predictions must be calibrated, simulations must remain coherent, and the product must make the resulting evidence legible to support real decisions. Aaru is a small, in-person team in New York that values urgency, high ownership, and intellectual honesty, expecting team members to surface inconvenient evidence, change their minds quickly, and carry important work to completion. Product Engineering at Aaru transforms simulation capabilities into user-independent and repeatable products. The team builds upon Aaru's shared platform and simulation systems to create workflows, interfaces, integrations, and decision-ready artifacts that make a sophisticated system clear and dependable. Work is organized around durable product domains, such as 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 limited frontend role; Product Engineers own user outcomes across the entire stack. Projects may involve observing customer decision processes, designing products and systems, developing new backend or orchestration primitives, evaluating model behavior, and managing measured production rollouts.

Requirements

Built and operated meaningful user-facing products, ideally from an early or ambiguous starting point.
Combine product judgment with strong software engineering fundamentals and can move comfortably between user workflows and system architecture.
Can work across a modern product stack and are willing to learn whichever layer is necessary to finish the job.
Seek out users, ask precise questions, and can tell the difference between what someone requests and what would actually solve their problem.
Have strong taste for simple workflows, clear interfaces, and the small details that make a product feel fast, trustworthy, and coherent.
Make sensible tradeoffs between iteration speed, quality, scope, maintainability, and technical risk—and can explain those tradeoffs plainly.
Treat instrumentation, evaluation, and production support as part of building the feature rather than work to be added later.
Comfortable with loosely specified problems, shifting information, and situations where the right answer must be discovered through prototypes and evidence.
Take responsibility for outcomes, including when the problem crosses team boundaries or the first approach fails.
Want to work in person in New York with a team that moves quickly and debates the work directly.

Nice To Haves

Experience as a founder, founding engineer, or early product engineer at a fast-growing company.
Experience building AI-native products, agentic workflows, copilots, or products in which model behavior materially shaped the user experience.
Fluency with technologies such as TypeScript, React, Python, APIs, workflow systems, relational data models, and cloud infrastructure—or equivalent depth in a comparable stack.
Experience with B2B or enterprise products involving permissions, auditability, integrations, collaboration, or complex configuration.
Experience with data visualization, analytical products, experiment builders, survey tools, research platforms, or decision-support systems.
A record of turning a customer-specific need into a reusable product capability adopted by many users.
Experience working closely with research or machine-learning teams and translating experimental capabilities into dependable product behavior.

Responsibilities

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.