Staff Site Reliability Engineer (SRE)

About the role

EarnIn’s products must deliver speed, reliability, resilience, and trust to community members who depend on them. As EarnIn grows, we cannot rely on heroics, tribal knowledge, manual investigation, or isolated SRE expertise. We must embed reliability practices that scale across product engineering teams, enhance customer experience, and enable rapid shipping without increasing operational risk. This role exists to lead EarnIn’s next stage of reliability maturity: an AI-first operating model that uses AI to actively detect, investigate, respond to, learn from, and prevent production issues. As a Staff Site Reliability Engineer, you will guide technical direction for reliability across critical services, relying on AI-assisted workflows as key tools to reduce toil, speed incident response, improve production readiness, and enhance the operational quality of the engineering organization.

Operate as a Staff-level technical leader: set standards, architect solutions, mentor engineers, influence work across teams, and build reusable systems and practices that multiply your impact beyond what you ship yourself. Bring AI-first thinking to reliability practices, using AI to speed up alert triage and incident investigation, automate runbooks, surface operational knowledge, improve postmortem quality, track corrective actions, quantify reliability through scorecards, catch capacity risks early, and analyze architectural risk. Keep human ownership and engineering judgment at the center of operations. AI helps engineers gather context faster, think more clearly, and repeat themselves less, but accountability stays with people. Partner with SRE, product engineering, infrastructure, security, and leadership to build reliability into how teams work, making it easy to adopt and impossible to ignore.

Responsibilities

• Define and evolve reliability standards across critical services, including SLIs, SLOs, error budgets, production readiness, observability, incident response, and resilience patterns.
• Establish a reliability operating model that clarifies service ownership, operational expectations, and decision-making around reliability tradeoffs for product engineering teams.
• Use AI-assisted analysis to interpret reliability trends, detect weak operational signals, highlight capacity risks using pattern recognition, and generate actionable reliability scorecards for teams, clearly delineating where AI automates data gathering and insight generation.
• Overhaul key stages of the incident lifecycle to achieve faster detection, sharper triage, richer context retrieval, clearer communication, and stronger follow-through.
• Command high-severity incidents as Incident Commander and reinforce the systems, tools, and practices that simplify incident management.
• Design and implement workflows in which AI assists with alert correlation, signal enrichment, root-cause exploration, runbook retrieval, postmortem drafting, and corrective-action tracking.
• Ensure AI-assisted incident workflows remain reviewable, auditable, and safe by requiring human verification at all critical steps and maintaining clear operational ownership with humans accountable for final decisions.
• Elevate on-call quality by silencing noisy alerts, automating repetitive investigations, and enabling responders to rapidly digest service context.
• Build tools that gather context from systems like Datadog, Cloud