Site Reliability Engineer

WHY this role exists

EarnIn’s community members rely on our products to deliver reliability and trust when they need them most. Reliability shapes the product experience, not simply operational concerns. Every noisy alert, unclear runbook, fragile deployment, or repeated incident undermines customer trust and hinders engineering teams.

This role enables EarnIn to build and run production systems with greater resilience, clarity, and confidence. As a Site Reliability Engineer, you will strengthen infrastructure, optimize tooling, deepen observability, streamline incident response, and elevate reliability standards. These actions empower teams to ship quickly and safely.

The base salary range for this full-time position is $189,000 - $232,000 plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View (Headquarters) and will require in-office work 2 days a week.

HOW you will create impact

• You will operate as a well-rounded SRE practitioner across production operations, observability, incident response, infrastructure-as-code, automation, and software engineering.
• You will demonstrate growing independence in reliability work. You will not only follow existing playbooks, but also refine them. You will transform production learnings into better alerts, clearer runbooks, safer deployments, stronger observability, and more reliable services.
• You will harness AI-assisted development and operational workflows to minimize toil, accelerate investigation, enhance documentation, and streamline infrastructure and reliability work. You will meticulously validate AI-generated output before applying it to production systems or operational workflows.
• You will collaborate with product engineering and platform teams to implement, explain, and support reliability practices, ensuring they are practical, understandable, and actionable.

WHAT you'll do

• Design and improve systems with resilience and graceful degradation in mind. Plan for capacity and possible failure modes.
• Define and measure SLOs and SLIs that reflect customer experience and help teams make better reliability tradeoffs.
• Use observability tools such as Datadog, CloudWatch, logs, metrics, traces, and APM. Build signal-heavy, noise-light visibility into production systems.
• Configure and improve alerting and routing through incident management workflows. Make sure pages are actionable, well-routed, and worth human attention.
• Participate in incident response from detection and triage through communication, resolution, postmortems, and follow-up.
• Continuously improve the incident lifecycle. Focus on better detection, clearer runbooks, stronger postmortems, and concrete remediations.
• Construct or optimize infrastructure, reliability tooling, and automation that eliminate toil and ensure operational consistency.
• Use AI-assisted tools to accelerate coding and documentation. Speed up root-cause exploration, runbook improvement, infrastructure-as-code workflows, and operational tasks.
• Help engineering teams improve production readiness and deployment safety. Support service ownership and operational clarity.
• Communicate reliability concepts clearly across technical and non-technical teams.
• Document operational knowledge to reduce silos. Make it easier for engineers to respond with confidence.
• Contribute to a culture where reliability is shared by SRE and product engineering teams.

WHAT we're looking for

• Bachelor’s or master’s degree in Computer Science, Engineering, or a related field, or equivalent industry experience.
• 3+ years of experience in SRE, Software Engineering, Infrastructure Engineering, or a related role.
• Hands-on coding experience in Python, Go, or similar production-oriented programming languages.
• Experience operating production systems and contributing to reliability, observability, incident response, infrastructure, or automation improvements.
• Working knowledge of SLIs, SLOs, error budgets, MTTR, and how reliability data informs engineering tradeoffs.
• Experience using logs, metrics, dashboards, traces, and alerts to diagnose production issues.
• Experience with distributed systems concepts such as retries, backoff, timeouts, graceful degradation, capacity planning, and failure isolation.
• Experience improving alert quality, runbooks, incident processes, and follow-through after production issues.
• Ability to communicate clearly, write useful documentation, and explain reliability concepts in plain language.
• Experience using AI-assisted development tools such as GitHub Copilot, Cursor, ChatGPT, Claude, or similar tools as part of your software development or operational workflow.
• Interest in using AI-assisted workflows to reduce toil, accelerate investigation, improve infrastructure-as-code workflows, and strengthen operational practices.
• Interest in mentoring peers and growing your impact across teams over time.