Staff Site Reliability Engineer

You will define how mission-critical machine learning and real-time analytics systems operate in production — influencing reliability strategy, deployment standards, and infrastructure architecture across engineering. This team operates in a highly collaborative, in-person engineering environment in SOMA. Infrastructure, ML, and engineering leaders work side by side to design, build, and operate complex systems in real time. The pace is fast, the feedback loops are tight, and decisions happen quickly. If you’ve grown from Linux systems to DevOps to Staff-level SRE, and you now think in terms of systemic risk, scalability, and long-term reliability strategy — this role gives you direct influence and visibility. This role is intentionally in-person because: Reliability decisions happen at architectural depth — not over Slack threads ML, data, and infrastructure teams collaborate continuously in real time Post-incident reviews, system design debates, and performance tuning sessions are hands‑on and high impact You will have direct access to engineering leadership and decision‑makers The infrastructure you’re operating is mission-critical and evolving quickly If you value deep technical collaboration, tight feedback loops, and being at the center of high-scale ML systems — this environment is built for that. What You’ll Own Production reliability for ML and real-time analytics workloads CI/CD strategy, deployment automation, and rollback design Observability frameworks (SLOs, alerting, monitoring, incident response) Infrastructure-as-Code and Kubernetes environments Capacity planning and performance optimization Post‑incident reviews that drive measurable, long‑term reliability improvements Reliability standards across teams — not just within a single service You’ll partner directly with engineering and data science teams to ensure ML workloads are production‑ready and reliable by design. What We’re Looking For Deep experience operating Linux infrastructure and networking in production environments Proven impact as a Staff SRE, Senior SRE, or senior‑level DevOps/Platform Engineer supporting distributed systems Experience supporting complex, data‑intensive or ML‑driven systems in production Strong hands‑on experience with Docker and Kubernetes Strong scripting ability (Bash and/or Python) CI/CD ownership experience (GitHub Actions, ArgoCD, or similar) Experience with modern observability stacks (Prometheus, Grafana, Datadog, ELK, OpenTelemetry) Ability to debug systemic failures across infrastructure, deployments, and workloads Clear communicator who works effectively across engineering and data teams Engineers who have evolved from infrastructure foundations into strategic reliability leaders will thrive here. These Skills Are a Plus Experience operating ML platforms at scale (training + inference) AWS or cloud‑managed services experience Exposure to data platforms such as Spark, Airflow, or Kafka Experience in SOC 2 or regulated environments Why This Opportunity Staff‑level ownership of mission‑critical ML infrastructure Direct influence over reliability standards across engineering High‑visibility role with architectural impact Collaborative engineering culture designed for speed and depth #J-18808-Ljbffr Valid8 Financial, Inc.