Staff Site Reliability Engineer

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.

Responsibilities

Act as a Staff-level technical leader: define standards, architect solutions, mentor engineers, influence cross-team efforts, and construct reusable systems and practices; Embed AI-first thinking into reliability practices; Maintain human ownership and engineering judgment at the center of operations; Collaborate with SRE, product engineering, infrastructure, security, and leadership teams; Define and evolve reliability standards across critical services; Overhaul key stages of the incident lifecycle; Command high-severity incidents as Incident Commander; Design and implement AI-assisted workflows; Elevate on-call quality and reduce toil; Steer service designs for graceful degradation, failure isolation, robust capacity planning, and operational safety in AWS; Mentor engineers in reliability practices; Set a reliability strategy with AI at the center; Redesign the incident lifecycle around AI-assisted speed; Push AI-first operations into product engineering teams; Architect for resilience at scale; Raise the bar through mentorship and standards

Requirements

7+ years in SRE, Software Engineering, or Infrastructure Engineering with increasing scope and cross-org influence; Demonstrated experience improving reliability and operational excellence at scale using clear KPIs such as MTTR, MTTD, alert quality, incident recurrence, SLO attainment, on-call health, or corrective-action completion; Shipped experience applying AI/LLMs to engineering or operational workflows, such as alert triage, runbook automation, incident investigation, postmortem drafting, remediation recommendation, operational knowledge retrieval, or agentic operations tooling; Significant expertise with SLIs, SLOs, error budgets, incident command, blameless postmortems, and recurrence prevention in large-scale distributed systems; Strong software engineering ability in Python, Go, or similar languages; Deep observability experience with systems such as Datadog, CloudWatch, OpenTelemetry, or similar platforms; Strong infrastructure-as-code and cloud infrastructure experience, including Terraform, Kubernetes, AWS, and safe, reversible deployment practices; Practical experience using AI-assisted development tools such as Cursor, Claude Code, Copilot, ChatGPT, or similar tools

Nice to have

Experience in fintech, regulated environments, SOC 2, PCI, FinOps, or cost/performance tradeoffs in high-scale systems is a plus

Benefits

Equity; Benefits