Jobs at AfterQuery

Design and build scalable data infrastructure for AfterQuery Experts site. Collaborate with research, product, and design to ship features, write maintainable code, participate in reviews, solve scalability and reliability challenges, and contribute to core infrastructure for data and evaluation used by frontier AI labs.

Own expert support for a frontier-AI expert network: manage queues and SLAs, resolve payment and project disputes, document and surface trends, build playbooks and escalation policies, and partner with operations and growth to reduce support volume and improve contributor experience.

Serve as primary relationship owner for strategic frontier AI lab customers, driving retention, expansion, renewals, and commercial negotiations. Build account management and customer health processes, run executive reviews, surface customer feedback to product and engineering, and lead cross-functional initiatives to shape customer engagement at scale.

Build production ML and data systems to measure and improve data quality. Develop expert-matching, quality prediction, anomaly detection, and evaluation infrastructure. Convert messy signals into models, metrics, and product improvements while partnering with engineers and domain experts and owning systems from design to deployment.

Own HR operations including payroll, benefits, compliance, HRIS integrity, and employee relations. Build HR infrastructure 0→1, create policies, manage investigations, and partner with finance and leadership to keep the company compliant and supported.

Lead GTM/RevOps execution: own CRM and pipeline hygiene, analytics and forecasting, RFIs, contract-to-cash, sales-stack automation, follow-ups, and customer engagement cadences to scale revenue operations and reduce manual work.

Lead supply-side growth for a talent marketplace: design sourcing strategy, build scalable acquisition channels and automation with engineering, forecast supply, optimize marketplace operations, recruit and manage a growth team, and transform manual playbooks into productized systems that increase contributor quality and retention.

Design datasets, evaluation rubrics, and reward signals for RLHF/RLVR; build real and synthetic data pipelines; run experiments modeling annotator behavior; develop quantitative metrics for dataset quality, diversity, and downstream impact; partner with research teams to translate training objectives into data and evaluation specs.

Partner with the CEO and operations team to run special projects end-to-end, build operational capabilities (billing, revenue ops, BD support), gather and analyze qualitative and quantitative data, support recruiting and administrative finance/office functions, identify and fix operational inefficiencies, and document and hand off durable processes.

Lead and deliver high-impact dataset and data-solution projects for frontier AI customers. Manage client relationships, coordinate specialist engineering and research teams, design dataset specifications, scope and execute projects end-to-end, and support cross-functional operational initiatives to drive revenue and product development.

Own and build People programs including compensation strategy, leveling and career pathways, performance management, learning & development, retention measurement, and org design. Partner with founders and finance to model comp/headcount tradeoffs, instrument programs as products, and iterate using data for an AI-native workforce.

Own end-to-end recruiting for business and operations functions, build proactive talent pipelines from elite finance/consulting/tech firms, design hiring profiles and rigorous interview processes, create recruiting infrastructure (scorecards, rubrics, tooling), track funnel metrics, and close high-caliber candidates while partnering with founders and executives.

Join the core engineering team to build and scale high-throughput data capture workflows (web and desktop), architect low-latency data pipelines, ship production features, and establish engineering best practices while owning end-to-end projects.

Manage and deliver high‑impact dataset and data‑solution projects for frontier AI labs. Scope, coordinate expert contributors, design dataset specifications, write production Python code, and drive projects end‑to‑end to influence foundation model training and product outcomes.

Define and execute go-to-market and brand strategy for technical and enterprise audiences. Launch and optimize multi-channel campaigns, create technical content (case studies, whitepapers, press), measure campaign performance, and represent the company at industry events while collaborating with founders and technical teams.

Own full-cycle technical recruiting for engineering and applied-AI roles: source, screen, coordinate interviews, manage offers, and build recruiting systems, pipelines, and metrics while partnering directly with founders and hiring managers.

Manage end-to-end interview scheduling and candidate logistics, maintain ATS records, coordinate interviewers and feedback, support recruiting operations, and improve hiring workflows and documentation to deliver a high-touch candidate experience.

Lead and manage a small engineering team while remaining hands-on: build and ship scalable production systems for applied AI, drive execution, recruit and mentor engineers, and collaborate across ops, domain experts, and customers.

Design and build full‑stack data capture workflows (Next.js web, Python desktop), architect high‑throughput data pipelines with low latency, collaborate on product strategy, ship features rapidly, and establish engineering best practices for team growth.

Design and run controlled SFT and RL post-training experiments to measure dataset impact on model capability, generalization, and alignment. Build public evaluations, publish research and reports, and iterate on data quality with internal teams and partner labs.