Clinical Research Scientist, Mental Health AI

About the Role 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. What You’ll Do 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. Requirements 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. Nice to Have 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. What We Offer 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 Relocation and transportation support Health and dental insurance Lunch and dinner provided, plus snacks, coffee, and drinks Unlimited PTO About Us Founding team : The core methodology behind this platform comes from NLP evaluation research we had done at Stanford. We raised a $5M seed from some of the top institutional and angel investors in the valley. Our team has prior work experience at NVIDIA, Meta, Microsoft, Palantir and HRT. Collectively, we have over 300 citations in our published work. Our early team include Stanford PhDs, ex‑Jane Street quants, and the first designer at Snorkel. Tech stack : We use Python for most things at Vals. Our platform is built on Django, with a React frontend. All of the infra is on AWS using CDK for IaC. 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 extremely 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. This is especially true for this role. 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. Further Reading: Hugging Face blog on evaluation Anthropic’s blog on challenges in evaluation New York Times article on issues in benchmarking Stanford HAI report showing hallucinations in legal tech tools #J-18808-Ljbffr Vals AI