Product Engineer

Product Engineer

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.

• 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.

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 conclusion back to the underlying simulation evidence.

• Integrate Aaru with a customer's data or operating system while handling authentication, permissions, data mapping, validation, retries, and safe writes.

• Diagnose a feature whose demo looks excellent but whose real-world completion rate is poor, determine whether the failure is in the model, workflow, interface, or expectation setting, and ship the right fix.

We begin with the decision the user is trying to make, not with a feature request. We seek direct evidence, reduce the problem to its essential uncertainty, and build the smallest system that can resolve it. We move quickly, but we preserve the foundations required for reuse and long-term ownership.

For AI-native products, product quality and model quality are inseparable. Latency, interaction design, evaluation, orchestration, permissions, reliability, and the behavior of the underlying models all shape the user experience. Product Engineers are expected to reason across these layers rather than treating model behavior as somebody else's API.

We…