Tenex

Tenex

Staff Site Reliability Engineer

Remote, USA · Staff+

Sponsorship not specifiedDetected 8 days ago
Distributed SystemsAWSGCPAzureCloud PlatformsKubernetesTerraformPrometheusGrafanaDatadogDevOpsSite Reliability EngineeringTemporalMachine LearningLLMsCybersecuritySIEMSOARSOC OperationsDetection EngineeringIncident ResponseSystems EngineeringLeadershipCommunication

About the role

  • Company Overview TENEX is an AI-native, automation-first, built-for-scale Managed Detection and Response (MDR) provider.
  • Seed round led by Andreessen Horowitz (a16z).
  • We're a small but well-funded team that just raised a substantial round - joining now comes with limited risk and unlimited upside.

Responsibilities

  • You will play a crucial role in designing resilient infrastructure, automating operational workflows, and shaping the future of our production environments while collaborating across engineering teams to drive technical excellence.
  • Design, build, and maintain highly available, scalable, and secure infrastructure to support our AI-native cybersecurity platform.
  • Develop internal tooling and automation to streamline deployment processes, incident response, and capacity planning.
  • Lead incident response efforts, conduct post-mortems, and implement long-term solutions to prevent recurring reliability issues.
  • Partner with sibling Engineering teams, Product, and Security teams to ensure reliability is baked into our development lifecycle from concept to production.

Requirements

  • 10+ years of experience in SRE, DevOps, or Software/Systems Engineering, particularly in managing production systems at scale.
  • Extensive experience with tools like Terraform, Pulumi, or similar technologies to manage complex infrastructure deployments.
  • Hands-on experience with monitoring, logging, and tracing stacks (e.g., Prometheus, Grafana, ELK, Datadog) to drive data-informed reliability decisions.

Nice to have

  • Domain Background: Prior work in cybersecurity, specifically regarding SIEM, EDR, or SOAR infrastructure.
  • AI/ML Infrastructure: Experience supporting infrastructure for large-scale AI/ML workloads (e.g., GPU scheduling, LLM serving optimization).
  • Startup Mentality: Background driving high-impact engineering initiatives in high-growth startups or enterprise SaaS.
  • Strong familiarity with Agentic Workflows such as Agno, Temporal, etc..
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • Relevant certifications (CKA/CKAD, AWS/GCP Professional Cloud Architect, etc.) are a plus.
  • Opportunity to work with cutting-edge AI-driven cybersecurity technologies and Google SecOps solutions.
  • A culture of growth and development, with opportunities to expand your knowledge in AI, cybersecurity, and emerging technologies.

Skills

  • Solid understanding of microservices architecture, distributed databases, and event-driven systems.
  • Clear, concise communication skills and a bias for collaborative problem-solving.
  • Strong problem-solving, debugging, and analytical skills, especially in high-pressure environments.

Compensation

  • Competitive salary and benefits package.

Company info

  • We are a force multiplier for defenders, helping organizations enhance their cybersecurity posture through advanced threat detection, rapid response, and continuous protection.
  • Our team is composed of industry experts with deep experience in cybersecurity, automation, and AI-driven solutions.
  • Backed by leading investors, we are rapidly growing and seeking top talent to join our mission of revolutionizing the AI-Native MDR landscape.
  • As an early employee, you'll play a meaningful role in defining and building our culture.
  • Culture is one of the most important things at TENEX.AI http://TENEX.AI-explore our culture deck at culture.tenex.ai http://culture.tenex.ai to witness how we embody it, prioritizing the irreplaceable collaboration and community of in-person work.

This listing is sourced directly from Tenex's careers page and normalized into a canonical job model.