Jobs at AfterQuery

Lead strategic engineering projects for an applied AI research lab, partnering with major AI organizations to build data solutions. Manage specialized expert teams, define dataset specifications, drive projects from scoping through delivery, and support cross-functional operational initiatives. The role requires life-sciences engineering expertise, quantitative process and systems reasoning, strong leadership, communication, and comfort operating autonomously in ambiguous environments.

Conduct a semester-long applied AI research project with an AfterQuery mentor in areas such as data quality, post-training, agent and foundation-model evaluation, or public benchmarks. Responsibilities include formulating research questions, designing experiments, developing datasets and evaluation methods, analyzing results, documenting reproducible work, and co-authoring a publication. Scholars receive mentorship, engineering support, annotation resources, and compute access.

Support research scientists with experiments involving advanced reasoning, computer use, and agentic AI models. Interns will collect, process, and analyze proprietary datasets; develop and test algorithms; document findings; present research; and collaborate with interdisciplinary teams. The role offers opportunities to contribute to publications and work with specialized datasets and computational resources. Interns work 10–40 hours weekly depending on company needs.

Designs, validates, launches, and improves rigorous evaluations and benchmarks for frontier AI systems. The role analyzes model capabilities and failure modes using experimental and statistical methods, builds reproducible evaluation infrastructure, collaborates on post-training measurement, and communicates findings through reports, technical articles, and research papers.

Lead strategic projects for AI data solutions, managing client relationships, specialized expert teams, dataset specifications, and end-to-end execution. Coordinate contributors across technical and professional domains while supporting operational initiatives that influence foundation model training and evaluation. The role requires advanced STEM expertise, quantitative reasoning, scientific research experience, strong leadership, entrepreneurial initiative, and familiarity with data-analysis programming and LLM workflows.

Corporate Counsel will serve as AfterQuery’s first in-house legal hire, managing commercial contracts, data licensing, DPAs, employment agreements, labor-law guidance, worker classification, and HR compliance. The role partners with Sales, Finance, and People teams, triages business legal questions, maintains contract templates, and coordinates with outside counsel on specialized matters including export controls, financing, and privacy investigations.

Build end-to-end product experiences supporting expert acquisition, onboarding, assessment, matching, activation, and retention. Partner with Growth and Operations to identify bottlenecks, instrument funnels, run experiments, and improve measurable outcomes. Develop internal tools and automation, create durable product primitives, and make thoughtful tradeoffs among speed, reliability, and maintainability while working closely with founders and cross-functional teams.

Lead AfterQuery’s enterprise data acquisition strategy, identify and prioritize target data assets, negotiate high-value purchase and licensing agreements, manage data partners, and ensure legal, IP, governance, and technical requirements are met. Own acquisition budgets, pipelines, forecasting, and ROI reporting while collaborating with Engineering, Operations, Legal, and Research to package data for foundation model training.

Lead strategic projects for leading AI organizations, managing client relationships and specialized expert teams. Define dataset specifications that influence foundation-model training and evaluation, own projects from scoping through delivery, and support high-impact operational initiatives. The role requires structured problem-solving, quantitative analysis, strong communication, entrepreneurial initiative, and comfort operating independently in an ambiguous, fast-moving environment.

Owns final quality reviews for datasets containing code, technical, reasoning, and knowledge-work data. Leads QC processes and tooling, partners with infrastructure engineers to automate workflows, identifies systemic quality issues, provides feedback to contributor training, and reports quality metrics, error rates, root causes, and trends to leadership. The role also supports cross-functional operational initiatives in a fast-moving AI research environment.

Run full-cycle recruiting for business and operations roles, including sourcing, screening, interviewing, candidate closing, and talent pipeline development. Partner with business leaders and the executive team to define hiring profiles, prioritize searches, improve interview rubrics and scorecards, support candidate experience, and track recruiting funnel performance. The role requires recruiting high-caliber candidates from finance, consulting, private equity, technology, and related fields in a fast-paced, five-day in-office San Francisco environment.

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.

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.

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.

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.

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.

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.