Jobs at AfterQuery

Design and build post-training infrastructure for supervised fine-tuning, reinforcement learning, and model evaluation. Develop reproducible pipelines for data preparation, versioning, sampling, training, checkpointing, and experiment tracking. Integrate systems with partner training stacks, model APIs, compute environments, and evaluation platforms. Run controlled experiments to measure dataset impact on model behavior, analyze noisy results, identify confounders, and produce actionable conclusions.

Own planning and execution of technical research programs spanning post-training, enterprise initiatives, fellowships, evaluations, and partner research. Translate ambiguous objectives into milestones, owners, dependencies, success criteria, and decision points. Track experiment progress, risks, blockers, partner dependencies, and releases while aligning researchers, engineers, program leads, and external partners. Apply working knowledge of LLM development, datasets, supervised fine-tuning, reinforcement-learning post-training, and evaluation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Design and scale expert acquisition pipelines combining manual and automated sourcing. Recruit professionals via LinkedIn and campus partnerships, run GTM experiments, manage purchasing, expand conference presence, collaborate with engineering and marketing to optimize product and brand, and represent the company at events.

Design, build, and own core infrastructure for large-scale data generation, evaluation, and human-in-the-loop systems. Architect distributed, high-throughput pipelines, shared developer platforms, and reliable production services. Ensure scalability, observability, and fault tolerance while collaborating with research and engineering teams to support AI experimentation and platform capabilities.

Lead client-facing, end-to-end strategic projects with frontier AI labs: scope, design dataset specifications, coordinate expert cross-functional teams, drive delivery and revenue, and support high-impact operational initiatives across engineering, AI, and business functions.