Top Tech Jobs & Startup Jobs in NYC, NY

Build and improve AI agent functionality, simulation pipelines, evaluation harnesses, and reliable production systems. Prototype and assess new initiatives, optimize throughput and performance, deploy solutions, and iterate using evidence. Work across infrastructure, APIs, frontend experiences, and model behavior while collaborating with engineering and deployment teams. Document technical designs, tradeoffs, dependencies, and risks.

Build and operate high-performance infrastructure for large-scale AI simulations. Design low-latency systems, improve data and computation pipeline reliability, identify bottlenecks across compute, networking, storage, and orchestration, and create developer tools and abstractions. Productionize research workflows, support distributed systems, and own infrastructure decisions from architecture through implementation and ongoing operation.

Build and lead Aaru’s enterprise sales organization, recruit and coach Account Executives, own revenue targets, and establish repeatable sales motions. Engage C-suite and government decision-makers, manage complex enterprise procurement, oversee forecasting and pipeline discipline, and partner with AI, product, marketing, legal, and deployment teams to drive customer adoption and expansion.

Lead the Artifacts product roadmap for AI-generated deliverables, including executive summaries, insight-generating documents, and interactive experiences. Collaborate with engineering, design, research, customers, and partners to establish evaluation and launch criteria, convert custom deliverables into reusable templates, debug model reasoning failures, identify platform gaps, measure adoption, trust, and quality, and present product strategy to executives.

Own strategic alliance and client relationships from discovery through deployment and expansion. Lead complex AI simulation engagements, joint selling, proposals, partner enablement, Centers of Excellence, governance, playbooks, and repeatable go-to-market systems. Collaborate with engineering, product, deployment, and commercial teams to translate partner needs into scalable solutions. Represent the company with senior executives and help partners embed the technology across their organizations and client offerings.

Serve as the technical bridge in pre-sales: lead technical discovery, design simulation-based POCs and demos, partner with commercial teams, support delivery handoff, relay client feedback to product/engineering, and communicate data-heavy findings to stakeholders.

Lead a focused team researching empirical prediction systems, including calibration, forecasting, subgroup performance, drift, and agentic prediction. Set research priorities, design rigorous experiments, build models from heterogeneous real-world data, and establish standards for temporal validity and prospective evaluation. Manage researchers, recruit and mentor talent, communicate evidence clearly, and transfer validated methods into production through cross-functional partnerships with population, evaluation, engineering, data, and product teams.

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.