Staff Site Reliability Engineer

As one of the first pioneers of earned wage access, EarnIn builds products that deliver real-time financial flexibility for those living paycheck to paycheck. Community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks.

We’re backed by world-class funding partners like A16Z, Matrix Partners, DST, and Ribbit Capital, and we’re growing fast to shape the next chapter of our journey.

About the role

EarnIn’s products must deliver speed, reliability, resilience, and trust to community members who depend on them. 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 to reduce toil, speed incident response, improve production readiness, and enhance operational quality.

Responsibilities

• Act as a Staff-level technical leader: define standards, architect solutions, mentor engineers, influence cross-team efforts, and construct reusable systems and practices that multiply your impact.
• Embed AI-first thinking into reliability practices, leveraging AI to streamline alert triage, accelerate incident investigation, automate runbooks, retrieve operational knowledge, enhance postmortem quality, track corrective actions, quantify reliability with scorecards, detect capacity risks, and analyze architectural risks.
• Maintain human ownership and engineering judgment at the center of operations. AI aids engineers by speeding context gathering and clarifying reasoning but does not replace accountability.
• Collaborate with SRE, product engineering, infrastructure, security, and leadership teams to embed reliability, making it easy to adopt and impossible to ignore.
• 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, and generate actionable reliability scorecards for teams.
• 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 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.
• 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, CloudWatch, incident.io, Slack, runbooks, deployment history, and service metadata.
• Transition teams from reactive paging to proactive reliability enhancement.
• Steer service designs for graceful degradation, failure isolation, robust capacity planning, and operational safety throughout EarnIn’s AWS environment.
• Apply production data, incident learnings, and AI analysis to spot architectural risks before they recur.
• Instruct engineering teams to embed reliability expectations into design reviews, launch protocols, and service evolution.
• Coach engineers in reliability practices, incident response, SLOs, observability, production debugging, and AI-assisted operational workflows.
• Direct design reviews, incident reviews, and operational maturity discussions to improve engineering judgment across teams.
• Produce documentation, tooling, and reusable patterns that unlock reliability knowledge and enable action.

Requirements

• 7+ years in SRE, Software Engineering, or Infrastructure Engineering with increasing scope and cross-org influence.
• Track record of KPI-driven reliability and operational excellence improvements at scale (e.g., 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. You build tools and automation, not just dashboards.
• Deep observability experience with systems such as Datadog, CloudWatch, OpenTelemetry, or similar platforms, with a bias toward signal-heavy alerting designed for real human response.
• 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 to accelerate your own engineering work and model effective adoption for partner teams.
• Experience in fintech, regulated environments, SOC 2, PCI, FinOps, or cost/performance tradeoffs in high-scale systems is a plus.

Benefits

• Base salary range: $252,000–$308,000
• Equity
• Comprehensive benefits

Schedule

This is a hybrid position in Mountain View (Headquarters) and will require in-office work 2 days a week.