Engineering Manager, Client Platform Engineering

OpenAI•San Francisco, CA

1d•$401,000 - $445,000•Onsite

About The Position

OpenAI’s Client Platform Engineering (CPE) team delivers trusted devices at scale: secure by default, reliable by design, and effortless to use. We own platform capabilities across macOS, Windows, iOS, Android, and Linux, spanning endpoint posture and device trust, application delivery, onboarding, updates, telemetry, workflow orchestration, and employee-facing remediation. The team partners deeply with Security, Research, Applied, and specialized engineering groups to enable and protect OpenAI while reducing friction for the people advancing our mission. As an Engineering Manager for CPE, you will lead a team of engineers responsible for the strategy, delivery, and operation of OpenAI’s cross-platform client foundation. You will combine people leadership with strong technical judgment: setting direction, developing engineers, reviewing architecture and tradeoffs, and creating the operating mechanisms that turn ambiguous needs into durable platform outcomes. This is a high-leverage role at the intersection of security, reliability, developer velocity, and employee experience. CPE is a highly technical platform engineering organization delivering first-party services, automation, observability, and safe fleet operations. We’re looking for a leader who can guide its next chapter, scaling the team and its systems, partnering across the company, and raising the bar for secure, reliable, low-friction experiences across every supported platform.

Requirements

Built and led teams that owned secure, reliable, mission critical services and infrastructure at scale, from strategy through production operations.
Delivered internal platforms, tooling, and services that materially improved reliability, security, and employee experience.
Made sound software and infrastructure engineering decisions at scale. Deep endpoint expertise is strongly preferred.
Raised production readiness through direct technical review of architecture, automation, code, IaC/GitOps, CI/CD, observability, and operational tradeoffs.
Operated infrastructure with material security or identity risk through staged rollouts, incidents, and decisions that balanced risk, compliance, and employee or developer experience.
Committed to joining the team’s engineering on call rotation after ramp up and using firsthand operational context to improve reliability and reduce toil.
Built inclusive teams that do excellent work, communicated with clarity and candor, and influenced partners across Security, IT, and Product/Engineering.

Nice To Haves

Deep experience with one or more client-platform tool ecosystems such as Jamf, Intune, Android Enterprise, FleetDM/osquery, Munki/AutoPkg, or Linux configuration management.
Experience building internal platforms or services for app delivery, device posture/trust, onboarding, notifications, workflow orchestration, or fleet telemetry.
Familiarity with cloud infrastructure, Terraform, Kubernetes/containers, service orchestration, and large-scale observability.
Experience partnering with security, compliance, or privacy teams in high-assurance environments.
Experience supporting research, developer, robotics, hardware-lab, or other specialized computing environments.
A record of contributing to open-source tooling, engineering communities, or cross-company technical standards.

Responsibilities

Lead and develop a high-performing engineering team; hire thoughtfully, coach engineers, create clarity, and foster an inclusive, high-accountability culture that pushes perceived limits.
Define and execute a multi-year client-platform strategy and roadmap across macOS, Windows, iOS, Android, and Linux, including how Codex and agents can reshape employee computing.
Provide technical direction for endpoint posture, device trust, application delivery, device onboarding, updates, fleet telemetry, workflow orchestration, and employee-facing remediation. Lead the team to rethink the platform from first principles as Codex and agent capabilities advance: what software needs to be built, what agents can own end to end, and which employee workflows no longer need to exist.
Guide architecture and engineering tradeoffs across first-party services, open-source tooling, SaaS platforms, IaC/GitOps, CI/CD, and cloud infrastructure. Decide what to build, buy, standardize, retire, or redesign as AI changes what the platform can do.
Establish clear goals, ownership, planning, design reviews, and execution rhythms that let the team move quickly without sacrificing quality.
Raise the bar for reliability and evidence-backed security through observability, drift detection, staged rollouts, release gates, rollback paths, incident learning, runbooks, and healthy operational ownership. Use telemetry to see friction, measure outcomes, and improve before issues become tickets.
Partner with Security, IT, Research, Applied, Product, and specialized engineering teams to align priorities and land company-wide changes, dogfood OpenAI products in real employee workflows, and continuously improve internal platforms.
Balance strong security and compliance controls with developer velocity, employee experience, and operational simplicity. Define where agents can act, where humans approve, and how actions are evaluated, audited, and reversed.
Use adoption, reliability, evidence-backed security posture, data freshness, employee experience, workflow completion, friction, agent effectiveness, and operational-load metrics to steer investment and communicate impact.
Partner with IT and Security teams to compound CPE’s impact across employee experience, security posture, and operational scale.