Staff AI Scientist, Content Safety

Overview

Join Intuit’s Content Safety team as a Staff AI Scientist, where you’ll build the intelligent infrastructure that keeps our global platform secure. We're looking for a specialist to design and deploy production-grade GenAI solutions that moderate content at scale. From pioneering automated labeling workflows to securing AI-generated outputs, your work will define the safety standards for every product in the Intuit portfolio.

Responsibilities

• Architect Robust Moderation Systems: Lead the design and deployment of sophisticated, multi-modal content moderation engines to detect and mitigate abusive content across Intuit’s diverse product ecosystem.
• Drive LLM Strategy: Direct the fine-tuning and optimization of state-of-the-art Transformers and Large Language Models (LLMs), balancing high-precision accuracy with production-scale efficiency.
• Pioneer Agentic Workflows: Research and integrate advanced GenAI patterns—including Retrieval-Augmented Generation (RAG) and Agentic AI—to revolutionize automated data labeling and complex classification taxonomies.
• Cross-Functional Leadership: Partner closely with ML Engineers, Product Leads, and Policy Experts, to help ensure AI systems aren't only technically superior - but also ethically grounded and compliant with global safety standards.
• Technical Evangelism and Research: Act as the team’s North Star by staying at the bleeding edge of AI research, translating academic breakthroughs into practical, "production-first" content safety strategies.
• Communication and Governance: Standardize high-quality documentation and model governance. You will translate complex technical architectures into clear, actionable insights for both executive stakeholders and technical peers.
• Team Upskilling and Mentorship: Serve as a force multiplier by mentoring junior and senior scientists, conducting rigorous code/design reviews, and fostering a culture of technical excellence.
  
  

• Advanced Academic Background: PhD or MSc in a quantitative field (Computer Science, Statistics, Applied Math), or equivalent industry experience in high-complexity AI environments.
• Proven Track Record: 5+ years of professional experience in Data Science or AI Research, with a history of delivering high-impact, production-grade models
• Cloud Architecture: deep expertise in architecting AI solutions on major cloud platforms (AWS, GCP, or Azure), specifically leveraging managed ML services (e.g., SageMaker, Vertex AI).
• Full-Stack ML Mastery: Mastery of Python and the modern AI stack (PyTorch, TensorFlow, Scikit-learn, Pandas). You should be comfortable designing distributed computing workflows and optimizing data pipelines for scale.
• GenAI Expertise: strong hands-on experience with LLM orchestration (e.g., LangChain, LlamaIndex) and fine-tuning. You don't just use LLMs; you understand how to steer them effectively.
• Safety & Alignment: a specialized focus on LLM security and safety frameworks.