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Lambda

Cloud-based GPU services for AI training

Senior Security Engineer - Detection & Response

Security Operations & Incident ResponseConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of hands-on security engineering experience specializing in detection engineering, threat hunting, or digital forensics and incident response.
  • Strong programming or scripting proficiency in Python, Go, Rust, or Bash, with a track record of building automated security tools and pipelines.
  • Deep technical knowledge of cloud infrastructure security, including AWS or GCP, Linux system internals, container runtime security, and network telemetry.
  • A clear understanding of the unique requirements of securing a cloud infrastructure provider.
  • Proven experience writing detection rules using SIEM, YARA, Sigma, Falco, or EDR queries, and managing detections through software development best practices.
  • Hands-on experience conducting forensic investigations and triaging complex, multi-stage attacks across cloud environments.
  • Ability to document complex vulnerabilities, incident post-mortems, and architectural designs clearly.
  • Ability to work in high-ambiguity environments while building structure and executing at startup pace.

Responsibilities

  • Design, build, and maintain detection rules, signatures, and behavioral analytics across Lambda's cloud, bare-metal, and internal infrastructure.
  • Conduct proactive threat hunting to uncover hidden threat actors, novel attack vectors, and zero-day vulnerabilities across multi-cloud and AI workloads.
  • Develop custom detection logic and playbooks as code to eliminate manual triage toil and reduce time-to-detect and time-to-respond.
  • Develop AI-powered detection and investigation workflows using internal LLM infrastructure to automate log analysis and threat intelligence context.
  • Lead technical incident response investigations for complex security events, performing forensic analysis, root-cause identification, and containment.
  • Architect automated response capabilities and orchestration pipelines to programmatically isolate compromised assets and revoke compromised credentials.
  • Partner with Platform and Infrastructure engineering teams to harden systems, build security instrumentation, and ensure telemetry coverage across services.
  • Participate in an engineering on-call rotation to maintain 24/7 operational readiness and rapid incident containment.
  • Conduct post-incident reviews, identify technical learnings, drive root-cause remediation, and prevent recurring incidents.
  • Mentor junior and mid-level security engineers, sharing technical domain expertise and promoting best practices in detection engineering and automation.

Desired Qualifications

  • Interest in using direct access to state-of-the-art LLMs for AI-powered threat hunting, automated security report generation, and intelligent vulnerability prioritization.
  • Experience building detection and response programs at AI or machine-learning companies.
  • Experience using AI or machine learning for security operations automation.
  • Experience scaling security during hypergrowth, including 10x growth phases.
  • Experience with eBPF, kernel-level telemetry, or specialized bare-metal host security tools.
  • Experience with security tooling build-versus-buy decisions.
  • Experience driving or providing significant evidence for compliance audits such as SOC 2, ISO 27001, PCI-DSS, HIPAA/HITECH, or FedRAMP.

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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