Research Scientist (Psychology) At Slingshot AI
The role
This is a rare opportunity to shape the future of psychologically intelligent AI. We’re looking for a Research Scientist with deep training in psychology and strong technical fluency, someone who’s excited to sit at the intersection of science and product.
You’ll join a team that’s evaluating and improving our model’s ability to reason, respond, and support users in psychologically meaningful ways. This is not a traditional academic or clinical role. Instead, you’ll be using your training in psychology to help us understand and improve model behavior; spotting risks, refining interventions, enhancing engagement, and defining what “good” looks like in AI-driven therapy.
You’ll split your time between research and applied product work, running studies, analyzing model behavior, collaborating with product and ML, and developing scalable processes for aligning model responses with real human needs.
About you:
- Advanced degree, PhD preferred, in Psychology or a related field, with a strong record of academic writing and publishing. You’re highly analytical, with experience running studies, evaluating interventions, and using statistical methods.
- Deeply technical, comfortable working with SQL and Python and interpreting machine learning concepts like recall/precision. Familiarity with LLMs or NLP models is a plus, e.g. a deep understanding of concepts like vector-based similarity
- Product-curious and user-focused, you care about how real people interact with AI systems and are motivated to improve those experiences.
- Clinical background or training is a plus (though this is not a traditional clinical role.)
- Comfortable moving fast, owning projects, and iterating quickly in an early-stage environment.
Key responsibilities:
- Design and run studies that evaluate model behavior, including alliance-building, safety, and efficacy of interventions.
- Analyze outputs and user interactions to identify strengths, gaps, and risks in model behavior.
- Collaborate with product and machine learning teams to develop processes that guide, monitor, and improve model outputs over time.
- Contribute to external communications through white papers, internal documentation, or publication when appropriate.
- Work with academic partners to drive ground-breaking research.
