Aaru
Simulation Engineer
About this role
Aaru seeks a Simulation Engineer to transform AI research into production systems that simulate human behavior at scale. You'll build the core infrastructure, abstractions, and evaluation frameworks that power population simulations using LLMs and classical methods, working closely with researchers to ship fast, accurate, and cost-effective solutions.
What you'll do
- Productionize the simulation loop including audience generation, world modeling, and response prediction
- Design reusable abstractions like agent/population objects, simulation interfaces, and evaluation harnesses
- Build and maintain evaluation and accuracy measurement infrastructure
- Optimize simulations for speed and cost at scale
- Partner with simulation research to harden hybrid LLM and classical architectures
- Create workflows and tooling for client simulation deployment and analysis layers
What they're looking for
- Machine learning and AI systems development
- Large language models in production
- ML experimentation and rigorous evaluation design
- Full model lifecycle management (data, training, evaluation, deployment)
- Cross-functional collaboration with engineering and product teams
- Multi-agent systems and probabilistic modeling
- Python and software engineering best practices
- Simulation, Monte Carlo methods, or agent-based modeling
Benefits
- Competitive base salary and equity participation
- Comprehensive medical, vision, and dental coverage
- Visa sponsorship and relocation support
- In-person collaborative environment in NYC
- Work on frontier AI technology with direct impact
- Small, mission-driven team with ownership and autonomy
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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.
View all jobs at AaruLikely interview questions
- Walk us through a production ML system you built from research to deployment. What were the key engineering decisions you made to productionize it?
- How have you designed abstractions and interfaces for ML systems? Tell us about a time when poor abstraction choices caused problems and how you fixed them.