• Own and operate our Kubernetes infrastructure: cluster lifecycle, upgrades, networking, and multi-tenant isolation for customer workloads • Build and maintain CI/CD pipelines and deployment infrastructure • Leverage AI to an extreme level to automate analysis and resolution of production issues, and improve software development speed, reliability and maintainability • Build dashboards, alerting, and anomaly detection across our systems • Define and enforce SLOs and build out incident response processes • Manage and improve our networking, load balancing, and service mesh configurations • Drive reliability improvements across the stack through automation, runbooks, and chaos engineering

• Own availability, latency, and throughput SLOs across a large fleet of generative media model APIs serving production traffic at scale • Build the monitoring, alerting, and observability needed to catch ML-specific failures, output quality degradation, pipeline breakage, model regressions before customers do • Harden model deployment workflows with canary releases, shadow testing, automated rollbacks, and validation gates so new model versions ship safely • Drive the security posture of the model fleet: secure model serving, abuse and misuse detection, rate limiting, and protection against adversarial usage patterns • Operationalize safety systems for generative media, content moderation pipelines, safety classifiers, and guardrails that run reliably at inference time without compromising performance • Lead incident response for model API outages and degradations, run postmortems, and drive the engineering work that prevents recurrence • Improve capacity planning, autoscaling, and GPU fleet efficiency for inference workloads under highly variable traffic • Partner with model and infrastructure teams to make reliability, security, and safety requirements part of how new models get onboarded to the platform

• Design and operate globally distributed backend services • Make architectural decisions on internal service integrations • Manage the full lifecycle: planning, implementation, monitoring • Maintain infrastructure tooling and automation • Contribute to engineering standards and documentation • Evaluate and integrate AI tooling into engineering workflows

• Run the Infrastructure & Services layer for internal data organization • Design, build, maintain, and document internal platforms with focus on automation and reliability • Own stability, availability, and security of services • Engineer systems to be resilient by design • Support services that Product teams depend on • Work directly with Analysts, Scientists, and Engineers • Drive cost optimization and capacity planning • Diagnose hardware faults and dispatch remote hands • Champion automation and engineering best practices • Help shape the structure, tooling, and future direction of the team