Site Reliability Engineer

Responsibilities

• 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.

Requirements

• 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.

Compensation

The base salary range for this full-time position is $189,000 - $232,000 plus equity and benefits.