Senior Research Scientist, Member of Technical Staff, 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 salarythe 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.

• 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…