About the AI Clinical Product Specialist at Headspace:
We are looking for an AI Clinical Product Specialist to help shape the future of AI-driven mental health support at Headspace. This role will focus on ensuring that our conversational AI experiences are clinically effective, engaging, and safe, using best-in-class prompt engineering, reinforcement learning, and human-in-the-loop methodologies.
As an integral member of the team developing our AI products, you will collaborate with clinicians, product managers, designers, data scientists, and engineers to define and implement clinical best practices and safeguards to uphold the quality and efficacy of our products and services.

REQUIRED SKILLS:

• Advanced graduate training in psychology, Masters, Ph.D. or Psy.D.
• 3+ years of experience in AI-driven product development, clinical AI, digital health, or related fields.
• Strong understanding of conversational AI models, NLP, and reinforcement learning.
• Experience in prompt engineering and designing AI-driven user interactions with a clinical or health-related focus.
• Knowledge of evidence-based clinical frameworks (e.g., CBT, mindfulness, behavioral science) and how they can be applied in digital experiences.
• Familiarity with AI ethics, responsible AI, and clinical safety best practices.
• Strong analytical skills, with experience in measuring and improving AI-driven experiences using data-driven insights.
• Excellent collaboration and communication skills—comfortable working across product, engineering, design, clinical, and research teams.

PREFERRED SKILLS:

• Experience working with LLMs (Large Language Models) and optimizing AI models in health tech applications.
• Familiarity with regulatory frameworks (e.g., HIPAA, GDPR, clinical safety guidelines for AI).
• Previous experience in a mental health, digital therapeutics, or health tech startup.
  Pay & Benefits:
  The anticipated new hire base salary range for this full-time position is $136,000-$190,000 + equity + benefits.
  Our salary ranges are based on the job, level, and location, and reflect the lowest to highest geographic markets where we are hiring for this role within the United States. Within this range, individual compensation is determined by a candidate’s location as well as a range of factors including but not limited to: unique relevant experience, job-related skills, and education or training.
  Your recruiter will provide more details on the specific salary range for your location during the hiring process.
  At Headspace, base salary is but one component of our Total Rewards package. We’re proud of our robust package inclusive of: base salary, stock awards, comprehensive healthcare coverage, monthly wellness stipend, retirement savings match, lifetime Headspace membership, generous parental leave, and more. Additional details about our Total Rewards package will be provided during the recruitment process.

Conversational Prompt Engineering:

• Drive conversational prompt engineering to enhance member experiences while ensuring clinical safety and efficacy.
• Partner with product development teams to develop AI interactions that align with evidence-based care practices.
• Continuously identify thematic areas for conversational improvement.
• Refine prompts and develop other strategies to prioritize and address these thematic areas based on user feedback, engagement data, and clinical guidelines.

Clinical AI Governance & Safeguards:

• Define and implement clinical best practices for our AI-driven experiences (AI Companion, Coach, and other conversational products).
• Work closely with cross-functional partners (AI/ML, Product, Design, Clinical) to develop risk mitigation strategies for AI-powered interactions.
• Establish guidelines for ethical AI use in mental health, ensuring compliance with clinical and regulatory standards.
• Partner with the QA team to develop and implement quality assurance processes for monitoring and reviewing the quality of AI-driven interactions, using insights to continuously improve product performance and safety.

Reinforcement Learning & Model Optimization:

• Design, implement, and manage the reinforcement learning (RL) process to optimize AI responses for quality, effectiveness, and safety.
• Partner with data scientists to analyze user interactions and sentiment data to improve model performance.
• Incorporate human-in-the-loop feedback mechanisms to continually refine AI-driven interventions.

Cross-Functional Collaboration & Research:

• Work across clinical, research, and AI teams to validate and test AI-driven experiences for real-world effectiveness.
• Stay up to date with the latest research in AI-driven mental health interventions, digital therapeutics, and conversational AI.
• Advocate for responsible AI use in mental health, balancing innovation with clinical rigor.

What you will bring: