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Aaru

Product Engineer

NYC$200k–$600kfulltimemidAdded today

About this role

Aaru seeks a Product Engineer to build end-to-end customer-facing products that translate AI simulation capabilities into usable workflows. You'll own projects from understanding user problems through design, implementation, and production launch, working across frontend, backend, and AI systems to deliver reliable, observable solutions.

What you'll do

  • Own product features end-to-end: discover user problems, design solutions, implement across the stack, launch, measure, and support in production
  • Work with users and customer teams to identify recurring needs and distinguish durable product opportunities from one-off requests
  • Build polished experiences spanning frontend, backend, APIs, data models, workflow orchestration, and AI-driven interactions
  • Design product behavior around AI uncertainty with clear states, human review points, fallbacks, and honest confidence communication
  • Define evaluation criteria before shipping model-dependent features using offline evaluation, product signals, and qualitative feedback
  • Instrument adoption, quality, latency, cost, and reliability to ground product decisions in evidence

What they're looking for

  • Full-stack product engineering (frontend, backend, APIs, data models)
  • AI/ML systems design and uncertainty handling
  • Workflow orchestration and complex system architecture
  • Product thinking and customer problem discovery
  • Metrics and observability design
  • Rapid prototyping balanced with production-quality engineering
  • Cross-functional collaboration and technical communication
  • Testing, debugging, and operational excellence
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Aaru

Aaru builds AI-powered population simulation technology that models human behavior at scale using LLMs and classical methods. The company is hiring Simulation Engineers, Platform Engineers, Data Integration Specialists, and Infrastructure Engineers to develop the core systems, data pipelines, and infrastructure that power these simulations.

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Likely interview questions

  • Describe a time you shipped a full-stack feature and discovered a critical assumption was wrong in production. How did you handle it?
  • How would you design a product feature when the underlying AI system has inherent uncertainty or variability?
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