Jobs at AfterQuery

Lead AI safety projects from client scoping through delivery, manage expert red-teaming teams, design adversarial datasets and safety benchmarks, and build repeatable human and automated red-teaming methodologies. The role requires hands-on model-jailbreaking or security research experience, strong writing and leadership, Python proficiency, and comfort defining ambiguous problems.

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.

Perform outbound prospecting and qualify leads for frontier AI labs. Research contacts, run multi-channel outreach (email, LinkedIn, X, events), maintain CRM hygiene, and develop outbound playbooks with founders. Articulate product value (data → model improvement) and hand off qualified pipeline to sales team.

Runs research programs spanning benchmark releases, partner collaborations, grants, fellowships, and research roadmaps. Coordinates projects end to end, maintains leaderboards, verifies evaluation results, manages expert capacity, tracks risks and resources, and unblocks technical work. Produces clear internal and external communications for researchers, executives, academics, and partners. The role also involves reading results tables, running evaluations, and contributing to benchmark design and publications.

Supports sales enablement, process execution, account and opportunity projects, reporting, and pipeline analysis. Creates sales collateral, coordinates RFIs and proposals, maintains accurate sales information, follows up across teams, identifies workflow improvements, and builds lightweight tools and processes. The role requires strong organization, communication, analytical, presentation, problem-solving, and prioritization skills in a fast-paced, ambiguous environment.

Own product areas end-to-end across Engineering, Operations, and Research. Identify high-impact problems, understand workflows, define product direction and technical requirements, prioritize using data and feedback, partner with engineers through launch, and improve scalability and workflows as the company grows.

Write and adapt technical and non-technical content across social media, blogs, founder communications, and company announcements. Translate research papers and technical findings into accessible writing, maintain distinct brand and founder voices, verify claims, collaborate with stakeholders, repurpose content across channels, and independently manage deadlines for a part-time contract.

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 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.

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.

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.