Senior Research Scientist, Member of Technical Staff, Stanford Law School

Legal Innovation through Frontier Technology Lab (liftlab), Stanford Law School The expected pay range for this position at the Law School is $144,362 to $199,386 per annum. Stanford University provides pay ranges representing its good faith estimate of the salary the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. Stanford Law School's Legal Innovation through Frontier Technology Lab (liftlab) is seeking a Senior Research Scientist, AI Agent Architect to architect, manage, and collaborate with a workforce of AI research agents in support of the lab's research agenda. AI research agents can now carry out much of the labor of research - literature review, data collection, coding, modeling, analysis, and drafting - at a scale and speed no individual can match. What they cannot supply is research judgment. This position is designed to fill that bottleneck: setting the research agenda alongside the Directors, decomposing problems into agent-executable tasks, designing the pipelines and guardrails under which agents operate, defining the standard of rigor agent output must meet, and auditing that output before it reaches a faculty member or a publication. The successful candidate will have conceived, conducted, and produced high-quality research end to end, and can credibly inform agents (translate a real research question into specifications and evaluation rubrics), audit agents (catch errors, non-reproducibility, and subtle invalidity), and collaborate with agents (exercise real-time judgment as results come back). The role is a microcosm of where liftlab believes knowledge work and the legal profession are heading: from an individual expert doing the work to an expert who directs and certifies AI systems that do the work. Core Duties Research Engagement: Partner with the lab Directors to translate substantive research questions into structured problems that can be decomposed into tasks executable by AI research agents, exercising independent judgment about which steps are appropriate for agent execution and which require direct human reasoning; attend lab and research-group meetings to scope agent-assisted workflows and identify where agents can responsibly accelerate inquiry. Solutions Development: Design and orchestrate pipelines of AI research agents that execute research tasks (e.g., literature review, data collection, coding, modeling, analysis, and drafting); write the task specifications, tool integrations, and guardrails under which agents operate; audit agent output for methodological rigor, reproducibility, and error, iterating on prompts, tooling, and workflow design to correct failures and improve reliability; read original academic papers and source code to validate results agents replicate, ensuring agent-produced work meets the standard of peer-reviewed research. Documentation and Training: Create the standards, playbooks, and evaluation harnesses that allow the lab to responsibly deploy research agents, keeping them current as agent capabilities evolve; develop and deliver documentation and training on agent-assisted research methods and on auditing agent output for rigor. Partnership and Collaboration: Coordinate interactions between lab researchers and AI/technology providers; evaluate platforms and agent tooling; co-create agent capabilities with researchers and communicate infrastructure needs to data and software professionals. Teamwork: Provide guidance and direction to more junior research staff and set the working standards for the lab's hybrid human-and-AI-agent team. Project Leadership: Contribute to the design and build-out of the lab's AI-research-agent infrastructure as a flagship initiative, setting the architecture for how agents are deployed, supervised, and evaluated across projects. Workforce and Team Management: Support the management and direction of staff who engage with AI agents; represent the lab's agent-assisted research model to internal and external audiences. * Other duties may also be assigned. Preferred Knowledge, Skills, and Abilities Demonstrated record of conceiving, conducting, and producing high-quality research end to end. Ability to translate substantive research questions into task specifications and evaluation rubrics suitable for AI agent execution. Ability to audit computational research output for errors, non-reproducibility, and subtle methodological invalidity. Strong programming and data skills, including machine learning methods, data collection and management, and research workflow design. Ability to read and understand original academic research papers and their associated source code, and to replicate results from diverse fields. Ability to exercise independent, real-time research judgment and to communicate standards of rigor to both researchers and technology providers. Preferred Qualifications PhD in computer science, data science, statistics, computational methods, or a related field, with strong familiarity with relevant tools and systems; or a Master's degree in computer science, data science, statistics, or a related field and a minimum of five years of relevant experience. Multiple lead-authored publications in leading academic venues in the general sciences, computer science, statistics, peer-reviewed legal journals and/or other relevant fields. Experience designing, deploying, or evaluating LLM-based or agentic AI systems in a research setting. Experience mentoring or directing junior researchers and setting team-level research standards. #J-18808-Ljbffr SLAC