Staff Site Reliability Engineer

Zefr•Marina del Rey, CA

3d•$190,000 - $210,000•Hybrid

About The Position

Zefr is the leading global technology company enabling responsible marketing in walled garden social environments. Zefr’s solutions empower brands to manage their content adjacency on scaled platforms such as YouTube, Meta, TikTok, and Snap, in accordance with industry standard frameworks. Through its patented AI technology, Zefr offers brands and agencies more accurate and transparent solutions for social walled gardens. The company is headquartered in Los Angeles, California, with additional locations across the globe. As a Site Reliability Engineer at Zefr, you’ll apply your expertise in cloud infrastructure, CI/CD, Observability, and core SRE concepts, to deliver high-quality, reliable, and scalable solutions. A significant aspect of this role involves working closely with the rest of Zefr’s Engineering and Data Science teams, ensuring the specialized infrastructure required for our services is robust, efficient, and scalable. We’re looking for someone to combine their technical expertise with strong leadership and a passion for continuous improvement and innovation. Zefr wants a candidate that champions reliability as a product feature, and can translate complex technical concepts into strategy. This is a role where you'll shape how we build and operate systems at scale. Support and build systems and tools that enable other engineers to generate, deploy, and manage product features and models both quickly and safely. Deploy and support a multi-cloud, micro-service architecture, including infrastructure tailored for ML workloads, deployed via Github Actions, ArgoCD & Kubernetes. Collaborate with other engineers, particularly the Machine Learning team, to architect secure, resilient, scalable, and cost-efficient applications and ML systems/pipelines in AWS and GCP. Foster and push our DevOps culture and philosophy by encouraging continuous improvement across all engineering teams. Proactively maintain the health of production environments, including monitoring application performance and resource utilization. Participate in 24/7 on-call rotation, respond to system performance issues and outages. Debug code at the application and infrastructure level. Mature our CI/CD workflows and release process. Maintians a forward-thinking approach, actively researching and proposing new solutions. Propose and review Engineering Request for Comments (RFC) to drive Engineering architecture and practices.

Requirements

7+ year job history designing, managing, deploying, and supporting Cloud Infrastructure in a production environment using major public cloud providers (GCP experience a huge bonus)
Knowledge of GitOps including an understanding of modern CI/CD pipelines, techniques and technologies (Github Actions, GitLab, CircleCI, Argo CD, Flux)
Proficiency with IaC and configuration management tools (Terraform, Terragrunt, OpenTofu, Crossplane, Pulumi)
Production experience architecting, managing, deploying, and supporting container based workloads into Kubernetes clusters
Strong problem-solving experience, focusing on automation
Proven track record of building and scaling reliability practices, including SLO/SLI frameworks, incident management, and capacity planning.
Heavy Production experience with observability platforms and practices (Prometheus, Grafana, Chronosphere, Datadog, OpenTelemetry); ability to design monitoring strategies for complex distributed systems.
Knowledge of cloud networking (Mesh, NAT, Load Balancers, API Gateways, proxies, etc), cloud security, and cost optimization strategies.
Strong written and verbal communication, organization, and documentation skills

Responsibilities

Support and build systems and tools that enable other engineers to generate, deploy, and manage product features and models both quickly and safely.
Deploy and support a multi-cloud, micro-service architecture, including infrastructure tailored for ML workloads, deployed via Github Actions, ArgoCD & Kubernetes.
Collaborate with other engineers, particularly the Machine Learning team, to architect secure, resilient, scalable, and cost-efficient applications and ML systems/pipelines in AWS and GCP.
Foster and push our DevOps culture and philosophy by encouraging continuous improvement across all engineering teams.
Proactively maintain the health of production environments, including monitoring application performance and resource utilization.
Participate in 24/7 on-call rotation, respond to system performance issues and outages.
Debug code at the application and infrastructure level.
Mature our CI/CD workflows and release process.
Maintians a forward-thinking approach, actively researching and proposing new solutions.
Propose and review Engineering Request for Comments (RFC) to drive Engineering architecture and practices.