Analytics & Automation Lead, User Safety & Risk Operations

OpenAI OpenAI · AI Frontier · San Francisco, CA · User Operations

Lead a senior technical operations team responsible for building analytics, automation, and quality systems to scale User Safety & Risk Operations. This role involves translating operational problems into practical solutions, guiding ICs, prioritizing needs, and building infrastructure. Key responsibilities include workflow automation, operational health analytics, quality and evaluation systems, emerging-risk signal infrastructure, dashboards, reporting, and data feedback loops. The role requires identifying opportunities for AI/LLM integration, designing appropriate controls, and establishing measurement frameworks for operational systems. Collaboration with various teams (Product, Engineering, Policy, etc.) is essential.

What you'd actually do

  1. Lead and develop a senior team of technical and analytical ICs responsible for automation, analytics, reporting, and other systems.
  2. Set the strategy and operating cadence for a horizontal team supporting safety and risk operations across multiple workflows and domains.
  3. Prioritize a portfolio of high-impact opportunities, making clear tradeoffs based on user impact, risk reduction, operational need, technical feasibility, and scalability.
  4. Build systems that improve operational health and visibility, including data infrastructure, dashboards, SLA and backlog monitoring, and quality measurement.
  5. Design and implement workflow improvements such as automated triage, routing, prioritization, signal enrichment, case clustering, review assistance, and reporting automation.

Skills

Required

  • SQL
  • dashboards
  • data quality
  • classifier/eval concepts
  • automation design
  • practical AI tooling
  • LLMs
  • agentic workflows
  • Codex-like tools
  • internal AI platforms
  • workflow automation
  • triage or routing systems
  • detection workflows
  • operational reporting
  • classifier feedback loops
  • human-in-the-loop review
  • monitoring
  • quality controls
  • fallback mechanisms
  • operational judgment
  • complex workflows at scale
  • queues
  • reviewer processes
  • escalations
  • capacity constraints
  • quality assurance
  • technical judgment
  • written communication
  • prioritization
  • execution

Nice to have

  • trust and safety
  • risk operations
  • data
  • analytics
  • product operations
  • integrity
  • fraud
  • people management
  • senior technical, analytical, or highly independent ICs

What the JD emphasized

  • highly technical for an operations role
  • fluent in using AI tools to improve real workflows
  • building systems that make safety operations faster, more reliable, more measurable, and more scalable
  • design appropriate evaluation, monitoring, human review, and fallback paths
  • Establish measurement and quality frameworks for operational systems
  • partner with operational leaders to understand their workflows, identify root causes and bottlenecks, and turn high-value needs into scalable systems rather than one-off analyses
  • Collaborate with Product, Engineering, Policy, Legal, Safety, Support, and other partner teams
  • Raise the bar for technical judgment, written communication, prioritization, and execution across a senior, independent team.
  • Experience working with LLMs, agentic workflows, Codex-like tools, or internal AI platforms to automate operational work.
  • built systems that materially improved how operational work gets done
  • distinguish high-value automation from risky automation, and know how to design human-in-the-loop review, monitoring, quality controls, and fallback mechanisms for sensitive workflows.
  • strong operational judgment and understand the realities of running complex workflows at scale
  • Communicate clearly and concisely with technical, operational, and executive audiences, especially when tradeoffs are complex or evidence is incomplete.
  • Operate with high agency in ambiguous environments and can create structu

Other signals

  • building automation
  • improving operational health
  • quality measurement
  • classifier and evaluation feedback loops
  • decision-support tools
  • workflow automation
  • operational health dashboards
  • queue and case analysis
  • alerting
  • reporting infrastructure
  • AI, LLMs, classifiers, or lightweight tooling
  • evaluation, monitoring, human review, and fallback paths
  • measurement and quality frameworks
  • golden sets, sampling strategies, reviewer calibration, false-positive and false-negative monitoring
  • system-health metrics