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Endpoint Engineer, IT

Thinking Machines Lab · San Francisco

CHECKED 13H AGO

Frontier labonsite$180k–$360k
Apply on the lab’s site ↗

At a glance

Highlights

  • Operate endpoint fleet as distributed platform with testing and staged rollouts.
  • Lead migration to new fleet platform with minimal disruption to employees.
  • Design zero-touch provisioning that makes new devices secure and productive fast.

What they offer

  • Generous health, dental, and vision benefits
  • Unlimited PTO
  • Paid parental leave
  • Relocation support as needed

Ideal candidate

A production-minded endpoint engineer who loves automation and a secure, seamless employee experience. You thrive owning complex initiatives and partnering across IT and Security to move fast safely.

Requirements

  • Python and shell scripting
  • macOS internals, including launchd, configuration profiles, TCC, system extensions, Endpoint Security, FileVault, Secure Token, and bootstrap tokens
  • Apple Business Manager, Automated Device Enrollment, and Apple's MDM and Declarative Device Management frameworks
  • Modern Apple MDM platforms, particularly Iru, Fleet, Jamf, or equivalent
  • Santa binary authorization and Rudolph synchronization infrastructure
  • Fleet-scale querying and osquery
  • Git, pull-request workflows, GitOps, and CI/CD for endpoint configuration
  • Terraform and infrastructure as code
  • Public-cloud fundamentals, including serverless infrastructure, containers, managed databases, and monitoring
  • Device lifecycle automation, including zero-touch enrollment, patching, software distribution, and secure deprovisioning
  • Endpoint security, Zero Trust, device trust, continuous posture evaluation, compliance, and automated remediation
  • Proficiency in Swift or Go for building macOS endpoint tools, agents, or supporting services
  • Experience using LLMs to automate operational work or interest in applying them to endpoint engineering
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Lab profile

About Thinking Machines Lab

Builds frontier AI that extends human will and judgment through customizable models and interfaces.

What they build

  • Inkling frontier models
  • Tinker model customization
  • Human-AI communication interfaces

Known for

  • Frontier model training
  • Post-training and reinforcement learning
  • Evaluation infrastructure
  • GPU supercomputing and reliability
More about Thinking Machines Lab →
Apply on the lab’s site ↗