Senior Clinical AI Engineer
New
I
Insight TimerHealth and Wellness
The role is fully remote, but you must be based in Australia.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- Backend DevelopmentPythonPrompt EngineeringLLM
Requirements
- 5+ years of software engineering experience.
- Production experience with AI agents, LLM-based systems, or AI-powered workflows.
- Strong Python fundamentals.
- 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 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.
Responsibilities
- 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.
- Improve prompts, tools, agent workflows, and retrieval systems.
- Architect flexible, modular workflows.
- Translate early AI prototypes into scalable, maintainable product systems.
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