Engineering Manager, Sensitive Deployments

OpenAI OpenAI · AI Frontier · San Francisco, CA · Applied AI

Engineering Manager for OpenAI's Integrity team, focusing on sensitive deployments of AI in high-stakes environments like government and regulated enterprises. The role involves leading a team of engineers to ensure safe and beneficial deployment of AI technologies, translating field learnings into reusable capabilities, and managing end-to-end delivery outcomes.

What you'd actually do

  1. Lead and grow a team of engineers responsible for complex, high-impact deployments of OpenAI technology in sensitive environments.
  2. Own end-to-end delivery outcomes across prototype, pilot, production launch, and ongoing operation.
  3. Partner directly with senior customer stakeholders, technical teams, and implementation partners to scope work, sequence delivery, and remove blockers.
  4. Set and maintain a high bar for architecture, code quality, testing, observability, reliability, security, and operational readiness.
  5. Translate field learnings into reusable tools, playbooks, reference architectures, product requirements, and roadmap feedback.

Skills

Required

  • 10+ years of engineering or technical delivery experience
  • 4+ years managing high-performing engineers
  • Led customer-facing, forward-deployed, platform, infrastructure, security, or applied AI teams through ambiguous production deployments
  • Experience working with government, defense, intelligence, public sector, critical infrastructure, or similarly sensitive/regulated customers
  • Credibly engage senior executives, mission owners, security stakeholders, and deeply technical ICs
  • Strong technical depth across full-stack software, cloud infrastructure, APIs, data systems, security, and production operations
  • Comfortable reviewing or writing production-grade code in Python, JavaScript/TypeScript, or similar languages
  • Understand deployment models involving Azure, AWS, Kubernetes, Terraform, identity, access controls, encryption, monitoring, and compliance requirements
  • Turn one-off customer work into reusable systems, platform capabilities, and durable team practices
  • Simplify complex work, make fast and sound decisions under pressure, and communicate tradeoffs clearly
  • Personally committed to the safe and beneficial deployment of AI

Nice to have

  • AI safety filtering
  • guardrails
  • agent orchestration

What the JD emphasized

  • sensitive environments
  • government deployments
  • regulated and high-trust enterprise settings
  • zero data retention and privacy-constrained deployments
  • increasingly agentic
  • high-consequence use cases relevant to government deployments
  • reusable Integrity capabilities for agentic detection and enforcements
  • ambiguous, high-priority initiatives
  • customer-specific needs
  • mission sensitivity is high

Other signals

  • deployment of AI systems
  • sensitive environments
  • government deployments
  • regulated enterprise settings
  • agentic detection and enforcements