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Product Psychologist

Slingshot AI

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Slingshot AI London Source published Sep 20, 2026 Verified 8 hours ago
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EmploymentFull-time

Overview

Slingshot AI

Full job description

Slingshot AI Slingshot AI is the team behind Ash, the first AI designed for mental health. Our mission is to make support more accessible and help people change their lives in the ways they want. We’re building a world-class team by empowering individuals with the autonomy, flexibility, and support they need to do their best work. We dream big, iterate fast, and care deeply. If that sounds like you, we’d love to hear from you. Our team spans machine learning, product, engineering, conversational design, clinical, growth, and operations, with offices in both New York City and London. We're a well-funded Series A company, having raised $93M from Andreessen Horowitz, Radical Ventures, Forerunner Ventures, plus top-tier tech investors involved in ElevenLabs, Captions, Shopify, Plaid, Notion, Canva, Twitch, Airtable, and many others. THE ROLE We are hiring a Product Psychologist to sit inside our machine learning team to elaborate on and help further build processes that answer: what does a great Ash conversation look like, and how do you measure it? Reporting to our Clinical R&D Lead and partnering closely our ML conversation team lead, you'll own the definition of quality for Ash's conversations and turn it into something the ML team can train and evaluate against through benchmarks, evals, and a distributed team of expert human reviewers. We think optimizing for clinical protocols is the wrong lever. We believe the core driver of outcomes in mental health support, AI or human, is more amorphous: the things that set individual practitioners, rather than individual practices, apart. This role exists to build a science of that belief into something iterable. This is not a role about designing conversations turn-by-turn. Ash is more grown than built. It's not a management role, though you'll coach a growing team of reviewers. And it's not a research role in the traditional sense. While you'll draw heavily on the literature, your aim is product outcomes, not papers published (though some of your work may become a paper in collaboration with our research team). KEY RESPONSIBILITIES

  • Own the definition of "great" for Ash conversations, and make it measurable.
  • Make hypothesis-driven calls on how Ash should behave, globally and in specific situations. Change your mind quickly and openly when the evidence tells you to.
  • Partner with the Safety team to define Ash's constraints and how it should handle difficult edge-case conversations.
  • Recruit, scale, and coach a distributed team of human reviewers: set the bar, teach people what "great" looks like, and keep the group calibrated with each other.
  • Be a source of clarity for the conversation and ML teams when the path forward is ambiguous. ABOUT YOU
  • You have a PhD in Clinical or Counseling Psychology, or related field, or a license-eligible Masters degree with clinical training and supervision
  • You’re an experienced clinician with a history of working with systems, technological and otherwise, to scale up engaging and effective health products
  • You have deep, hands-on experience with interpersonal skills. You can personally facilitate emotional expression well, and you can also see it in someone else's work: flexibility, warmth, honesty, confidence, and attunement, not just credentials.
  • You're pragmatic and decisive under real uncertainty. You can make a call with incomplete information, defend it, and drop it fast when new evidence shows up.
  • You're oriented toward the user, not toward an external clinical ideal. You won't push an agenda that matters more to you than it does to the people using the product.
  • You've worked on measurement before, ideally in digital mental health, and you know how to build a rating system and keep a team of raters calibrated.
  • You have some comfort operating close to an ML team. You don't need to train the models, but you should be curious about how evals and benchmarks actually get used to change model behavior.

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