| Location | San Francisco, California |
| Website | https://www.vals.ai/home |
We’re looking for a Clinical Research Scientist to help lead and expand our work evaluating AI systems in mental health and other clinically sensitive settings.
As people increasingly use AI for emotional support, health information, and guidance during periods of distress, we need better ways to determine whether these systems respond safely and appropriately. Many important risks emerge over the course of a conversation, including missed signs of escalating distress, reinforcement of harmful beliefs, diagnostic overreach, and unhealthy emotional dependence.
You’ll work closely with our existing research team and engineers to build clinically grounded benchmarks, realistic multi-turn scenarios, scoring criteria, and validation studies. You’ll help define what safe and unsafe model behavior looks like and ensure our evaluations reflect meaningful clinical risks.
This role is a strong fit for a clinical psychologist, psychiatrist, or clinical scientist who wants to help shape a growing research program in mental health AI evaluation while maintaining active academic and clinical collaborations.
Lead the clinical design of evaluations for AI systems used in mental health and other sensitive domains.
Identify important clinical failure modes and translate them into realistic scenarios and clear scoring criteria.
Design studies to validate evaluation methods, including clinician review, inter-rater reliability, and comparisons with real-world interaction data.
Work with psychologists, psychiatrists, researchers, and academic collaborators to develop rigorous benchmarks.
Partner with technical researchers and engineers to implement evaluations at scale.
Analyze model behavior, publish findings, and help shape our broader research agenda.
PhD, PsyD, MD, or equivalent research training in Clinical Psychology, Psychiatry, Behavioral Science, Public Health, or a closely related field.
Experience designing and leading empirical research, including study design, analysis, and scientific writing.
Strong grounding in psychopathology, clinical assessment, risk evaluation, or evidence-based mental health care.
Track record of peer-reviewed research.
Experience with clinical, behavioral, quantitative, qualitative, or psychometric research methods.
Ability to translate clinical concepts into clear, testable evaluation criteria.
Research in adolescent mental health, suicide or self-harm, psychosis, eating disorders, trauma, or other clinically complex areas.
Experience developing or validating clinical measures, rating systems, or coding frameworks.
Experience with longitudinal research, conversation analysis, or real-world behavioral data.
Experience studying AI or other digital technologies.
Familiarity with large language models or AI evaluation.
Experience leading IRBs or cross-institutional collaborations.
Significant ownership over the direction of our mental health AI research
Close collaboration with clinical, technical, and research colleagues
Strong technical support and resources for large-scale studies
Competitive salary, meaningful equity, and title flexibility based on experience
Health and dental insurance
Lunch and dinner provided, plus snacks, coffee, and drinks
$1,500 housing stipend (within one-mile radius of our office)
Unlimited PTO
Vals AI builds rigorous evaluations and benchmarks for frontier AI systems. Our work started from NLP evaluation research at Stanford, and today we work across technical and domain-specific areas, including healthcare and mental health. We’ve raised a $5M seed and our team has backgrounds at Stanford, NVIDIA, Meta, Microsoft, Palantir, HRT, Jane Street, and Snorkel.
We recently announced our $40M Series A at a $400M valuation, led by Andreessen Horowitz, with participation from existing investors 8VC, Pear VC, and Bloomberg and new investors Hudson River Trading and NextLadder Ventures.
What We're Looking For
Learning velocity: The role encompasses a wide variety of tasks. Rather than expecting you to be an expert on Day 1, we are looking for someone who can learn new skills and technologies quickly.
Ownership: Working in a small, talent-dense team, we expect everyone to show initiative to build where it's needed, not where it's asked. We strive for autonomy over consensus.
Intensity: The LLM landscape is constantly changing. Foundation model labs are continuously pushing the frontier. The unicorn companies that will emerge from this technology shift are being built now. Those that win will have an incredibly high speed of execution.
Solution-oriented mindset: We're looking for people who see opportunities to craft solutions at each juncture, not those who pass hard problems to others or admit defeat.
Vals in the Media: