Jobs at Aaru

Own end-to-end engagements with law firms, translating litigation strategy into AI simulations, delivering findings to senior legal leaders, and developing long-term client relationships. Present recommendations, coordinate with engineering, product, and commercial teams, identify new opportunities, and support law firm brand and go-to-market strategy. The role requires a former or current complex litigator interested in applying predictive technology to litigation and regulatory strategy.

Coordinate the end-to-end candidate experience, including interview scheduling across time zones, candidate communications, ATS and recruiting data management, onboarding support, and recruiting process improvement. Partner closely with recruiters, interviewers, and hiring managers while managing logistics, administrative tasks, and employer branding support in a fast-paced, high-growth environment.

Own end-to-end technical recruiting across engineering and research in the US. Responsibilities include targeted sourcing, candidate and interviewer experience, interview process management, offer negotiation and closing, offer letter generation, and building trusted relationships across the organization. The role requires 4+ years of full-cycle technical recruiting experience, strong storytelling and influencing skills, candidate empathy, creative use of AI, and comfort working in a fast-moving environment.

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.

Own client relationships from discovery and scoping through deployment and long-term collaboration. Deliver high-quality, simulation-driven analyses to senior stakeholders, translate ambiguous direction into client-ready outputs, coordinate engineering, product, and commercial teams, and identify new engagement opportunities. The role is fully in-person in San Francisco and involves working closely with company founders and clients across industries.

Lead methodological strategy for AI-driven human behavior simulations during the sales process. Design audiences, stimuli, validation frameworks, proof-of-concept evaluations, and comparisons with traditional research methods. Translate business questions into rigorous simulation designs, explain statistical validity to senior stakeholders, support account executives and deployment teams, and convert field feedback into product priorities. Contribute to public methodology, accuracy frameworks, and validation publications.

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.

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.