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Lead Aaru’s Simulation Engineering team and own the quality, scalability, reproducibility, observability, and operational health of production AI simulations. Set architecture and productionization standards, build evaluation and regression systems, improve large-scale simulation performance, manage incidents and releases, collaborate across research and engineering, and recruit and develop high-performing engineers. The role remains hands-on, involving technical design, coding, debugging, and difficult tradeoffs among fidelity, calibration, latency, cost, and reliability.

Lead and develop a small team of Product Engineers while remaining hands-on in architecture, coding, debugging, and technical direction. Partner with Product and Design to define roadmaps, build reusable customer-facing capabilities, and establish quality standards for AI-native products. Own production reliability, security, permissions, latency, cost, observability, and incident follow-up. Recruit and mentor engineers, improve execution across workstreams, and create durable interfaces with platform, simulation, research, and deployment teams.

Own research on forecasting aggregate human behavior and future outcomes. Build datasets and predictive methods, including statistical models, language models, retrieval, and agent simulation. Design temporal holdouts and prospective evaluations; assess calibration, robustness, subgroup performance, uncertainty, and decision costs. Diagnose failures, compare complex approaches with strong baselines, document reproducible findings, communicate negative results, and hand validated methods to engineering for production deployment.

Own ambiguous customer problems end to end, from user discovery and system design through implementation, evaluation, rollout, measurement, and production support. Build full-stack, AI-native product experiences involving interfaces, APIs, data models, orchestration, permissions, integrations, and reporting. Establish evaluation criteria, instrumentation, reliability practices, and reusable product primitives while collaborating with product, design, platform, simulation, research, and customer-facing teams.

Lead and grow the Platform engineering team building APIs, workflows, and interfaces that make simulations usable. Set technical direction, make architecture decisions, write code, turn research into reliable product features, hire and mentor engineers, and deliver high-quality production systems in an in-person NYC environment.

Design and build the platform that powers AI-driven simulation products. Create abstractions and framework-level code, partner with research and product to integrate simulation capabilities, and own platform architecture through implementation and iteration to make complex capabilities accessible to diverse users.

Lead product design across Aaru's AI-native platform: identify user patterns, ship prototypes and features, evolve the design system, experiment with agentic workflows, and hire/build the design team.

Own the full enterprise sales cycle for Aaru's AI-driven behavioral simulation product: prospecting, discovery, scoping, pitching, negotiation, and close. Develop targeted outbound strategies, manage multi-stakeholder executive deals, expand market segments, refine go-to-market playbooks, and maintain disciplined pipeline hygiene, forecasting, and CRM-driven account plans.

Source and secure high-value third-party datasets by mapping the data landscape, outreaching to vendors, coordinating sample reviews, evaluating data quality and value, negotiating licensing and commercial terms, and maintaining vendor records while partnering with research, engineering, and leadership to prioritize acquisitions.

Build and maintain scalable pipelines to ingest, clean, and integrate large multimodal datasets. Own ingestion across APIs, files, cloud storage, and warehouses. Design linkage, entity resolution, deduplication, and schema harmonization. Implement data quality checks, validation, and documentation. Collaborate with engineering, research, and deployment teams to prepare integrated data for simulations and evaluate new data sources.