From Stanford University's posting. “We” and “our” refer to the employer.
Develop, create and provide targeted and advanced computational, analytic and data-focused consultation, requirements analysis, innovative technical solution development, code optimization, instruction and tooling to Stanford research to enable the leveraging of advanced computing technologies to support and accelerate research and scientific discoveries.
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.
Responsibilities
Core Duties:
Researcher Engagement:
Consult and collaborate with faculty and researchers to understand their research goals and identify technical obstacles and solutions.
Attend research groups’ meetings and presentations to assist with identifying promising tools and systems and to discuss their computational challenges and requirements.
Engage with researchers on the use of a broad set of cyberinfrastructure systems, tools, and software.
Provide support for Stanford research computing clusters and storage services, cloud computing, and national resources.
Solutions Development:
Formulate innovative technical strategies and engineer them to completion to achieve unique research objectives, using external vendors as needed. Provide novel technical solutions and consultation on complex technical topics requiring solutions that combine multiple computational tools, approaches and techniques.
Consult to develop frameworks that assist researchers in analyzing and visualizing data to uncover insights and support research findings. Consult on the creation and management of large, variable datasets in order to enable their preparation and use in research for modeling and analysis.
Help facilitate the design and debugging of research workflows with researchers.
Research and assist in the development and implementation of innovative technical solutions to meet research needs, including machine learning algorithms, text and image processing techniques, API programming, custom full-stack applications, automated web scraping, and crowdsourcing pipelines. Identify appropriate computational platforms
(locally and externally) and facilitate researchers' use and mastery of same.
Continuously adapt to evolving technologies and methodologies to accelerate research development and enable new research frontiers.
Leverage technical knowledge and interpersonal skills to support and enhance academic research. Review product demos and provide initial evaluation of potential platforms.
Read and understand original academic research papers and their associated computer source code. Replicate results from diverse fields as needed.
Documentation/Training:
Contribute to research and development efforts to enhance the team's capabilities in research support, including creating tutorials, testing new data collection methods, and staying abreast of relevant literature. Develop and deliver documentation and training for faculty and research staff.
Enhance learning with full awareness of the local research computing and data landscape; continuously optimize the offerings.
Help develop and deliver research community learning opportunities, including the delivery of workshops, bootcamps, the creation of videos and other learning collateral, and technical documentation.
Partner with others to create training schedules, collaboratively develop materials, and lead training sessions focused on the use of Stanford cyberinfrastructure services for researchers.
Partnership/Collaboration:
Assist colleagues and more junior team members by providing guidance and direction on project activities.
Connect and coordinate interactions between researchers and technology providers.
Provide regular communications to the systems and software/data professionals.
Partner with researchers to co-create and co-learn relevant computing and data capabilities.
Teamwork:
Assist colleagues and more junior team members by providing guidance and direction on project activities.
Minimum Education:
Bachelor’s degree and 5-7 years of experience in computational methods, tools and systems or a Master’s degree in computer science, data science, statistics, or a related field
Minimum Experience:
3-5 years of relevant experience, or combination of education and relevant experience.
Knowledge, Skills and Abilities:
High level of proficiency in data science and machine learning methodologies and tools.
Strong programming skills in Python. Strong and demonstrated experience with scientific coding/scripting, preferably in Python, R, Fortran, C++ and various shells. Experience with research software written in one or more of these: Matlab, R, Julia, Javascript, Stata.
Experience with research software written in one or more of R, Stata, Matlab, SAS, Julia, JavaScript.
Experience installing and debugging installations of complex scientific applications and associated dependencies. Demonstrated expertise preferred in creating and debugging application containers and scientific workflows in a cluster computing or cloud-based advanced analysis environment.
Experience with data processing at scale, and an understanding of which tools are appropriate at which times.
Working knowledge of OpenMP, MPI or other parallel processing approach sufficient to advise on SLURM submissions and basic debugging techniques. Working knowledge of at least one mainstream ML/AI framework and how to execute efficiently in an advanced computing environment.
Proficient in debugging SLURM errors associated with complex jobs and recommending solutions.
Familiarity with text and image processing techniques (OCR, NLP, regex, image classification).
Ability to develop and deploy full-stack applications and API integrations for a research setting.
Excellent problem-solving capabilities and creativity in developing bespoke solutions.
Strong interpersonal and communication skills for effective collaboration with a diverse group of stakeholders.
Commitment to continuous learning to understand the latest technologies and research methodologies including research computing.
Service-oriented and empathetic, comfortable helping researchers with varying skill levels.
Demonstrated ability to work and collaborate with others across the workgroup, the organization, and user community; ability to prioritize tasks and manage time effectively; and strong interpersonal and communication skills for effective collaboration with a diverse group of colleagues and stakeholders.
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