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Lambda

Cloud-based GPU services for AI training

Senior Security Engineer - Enterprise Security

Security Engineering & ArchitectureConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of demonstrated security engineering experience in enterprise or corporate security, with controls employees continue to use.
  • Independently own security work end to end, make sound decisions independently, and know when to escalate.
  • Thrive in high-speed, high-ambiguity startup environments where priorities shift and structure must be built while executing.
  • Build security employees can trust by being transparent about monitoring and its purpose, respecting privacy, and enlisting people to fix problems rather than locking them out.
  • Have hands-on depth in workforce identity, including identity providers and single sign-on such as Okta or Entra ID; federation and provisioning standards such as SAML, OIDC, and SCIM; multifactor authentication; and privileged access.
  • Have practical experience securing corporate software-as-a-service, managed endpoints using mobile device management and endpoint detection and response, and network access built on zero-trust principles.
  • Ship high-quality automation in Python, Go, or a similar language, including security work with large language models or agents that was impractical to do by hand.
  • Understand how people use AI assistants and agents at work and where they create data and access risks.
  • Translate security trade-offs into terms IT, HR, Legal, and employees can act on, earning adoption with and without authority.

Responsibilities

  • Make safe use of AI tools and agents easy by providing approved tools, connectors, and agent environments.
  • Ensure humans, agents, and integrations move sensitive data only along reviewed paths consistent with Lambda's data protection strategy.
  • Build automation and evidence that access in every application changes when a person's job changes and ends when they leave.
  • Determine what a person, device, contractor, or agent must prove before reaching company systems, then engineer the checks and access paths that enforce those requirements.
  • Keep corporate devices hardened, monitored, and patched, building on the mobile device management and endpoint detection and response platforms run by IT.
  • Continuously identify unmanaged software-as-a-service, risky configurations, and sensitive data shared beyond appropriate boundaries, and drive each finding to resolution.
  • Turn contested trade-offs, such as when a managed device is required, into clear rules with a defined exceptions path.
  • Ship Python or Go automation on top of platforms run by IT, using Lambda-hosted large language models, frontier models, and AI tools.
  • Measure control coverage and effectiveness, and partner with governance, risk, and compliance to ensure audit evidence comes directly from controls.
  • Ensure corporate systems produce the telemetry needed by Detection and Response, and help respond to incidents in the role's area.
  • Help IT and engineering partners build secure instincts, and mentor newer members of the security team.

Desired Qualifications

  • Experience securing contractor or personal-device access without managing the device.
  • Experience controlling where data flows in AI-heavy or software-as-a-service-heavy environments through data perimeter, data loss prevention, or similar controls.
  • Experience isolating developer or agent workloads.
  • Experience managing identity, software-as-a-service, or endpoint configuration as code, such as with Terraform.
  • Experience supporting SOC 2, ISO 27001, or FedRAMP audits with evidence from corporate controls.
  • Enterprise security experience at a high-growth technology company or cloud provider.
  • Curiosity about how securing a workforce changes when much of its work is done by AI agents, and enthusiasm for helping determine the approach.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

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