Senior Clinical AI Engineer
Remote · Sydney, New South Wales, Australia
Job Summary
Design, build, and ship production-grade AI agents for meditation, wellbeing, mental health, sleep, anxiety, stress, and behavior change; build agentic workflows that combine technical reliability with clinical and evidence-informed reasoning; integrate AI agents with backend APIs, data systems, content systems, and product surfaces; build evaluation, testing, and monitoring systems for reliability, safety, quality, and continual improvement; design clinical and wellbeing evaluation rubrics for AI outputs, including clarity, usefulness, safety, evidence alignment, and risk; work with the Head of AI, clinical research team, product, and engineers to translate clinical and research requirements into product and system design; identify risks in AI-generated wellbeing outputs, including unsupported claims, unsafe recommendations, overstatement, inappropriate advice, and poor handling of vulnerable users; improve prompts, tools, agent workflows, and retrieval systems based on both engineering performance and clinical/product quality; architect flexible, modular workflows that make it easy to test, extend, and improve AI capabilities; help define when agentic systems should answer, ask for more context, escalate, refuse, or redirect; design data and feedback pipelines that improve agent performance through real-world usage, evaluation, and, where appropriate, fine-tuning; translate early AI prototypes into scalable, maintainable product systems used by millions of people; help shape Insight Timer’s internal standards for building safe, evidence-informed AI wellbeing products.
Required Qualifications
- 5+ years of software engineering experience, including production experience with AI agents, LLM-based systems, or AI-powered workflows
- Strong Python fundamentals and experience building reliable backend systems, APIs, or distributed workflows
- Hands-on experience with agent frameworks, orchestration tools, evaluation pipelines, prompt engineering, retrieval systems, or LLM application architecture
- Formal training, research experience, or applied expertise in psychology, medicine, psychiatry, neuroscience, behavioral science, digital health, mental health, meditation, mindfulness, or wellbeing science
- Strong judgment around clinical or wellbeing claims, evidence quality, risk, user vulnerability, and real-world applicability
- Comfortable evaluating AI outputs not only for technical correctness, but also for safety, clarity, appropriateness, and evidence alignment
- Able to translate clinical, psychological, or research concepts into product requirements, prompts, workflows, and engineering decisions
- Comfortable working across engineering, product, research, and clinical domains
- Pragmatic and product-minded. You know when to move quickly, when to slow down, and when quality or safety needs to come first
- Motivated by building systems that help millions of people live healthier, calmer, and more meaningful lives
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