BranchFactor Summary

The original posting with the fluff stripped out

Expected experience: 8+ yrs
Management level: Individual contributor

Compensation

Base salary	US$152,000 – US$228,000 / yr

Role speciality

AIOpsIncident ManagementObservabilityOperational IntelligenceService Reliability Engineering

Useful technologies

Amazon Bedrock AgentCoreAWSCI/CDConfluenceGCPGoJiraKubernetesPython

Original posting

About IonQ:

IonQ, Inc. [NYSE: IONQ] is the world’s leading quantum platform and merchant supplier - delivering integrated quantum solutions across computing, networking, sensing, and security. IonQ’s newest generation of quantum computers, the IonQ Tempo, is the latest in a line of cutting-edge systems that have been helping customers and partners including Amazon Web Services, and AstraZeneca achieve 20x performance results and accelerate innovation in drug discovery, materials science, financial modeling, logistics, cybersecurity, and defense. In 2025, the company achieved 99.99% two-qubit gate fidelity, setting a world record in quantum computing performance.

Headquartered in College Park, Maryland, IonQ has operations in California, Colorado, Massachusetts, Tennessee, Washington, Italy, South Korea, Sweden, Switzerland, Canada, and the United Kingdom. Our quantum computing services are available through all major cloud providers, while we also meet the needs of networking and sensing customers across land, sea, air, and space. IonQ is making quantum platforms more accessible and impactful than ever before.

Location: This role is based at our Santa Clara, CA office, with the option to work a few days a week remotely.
Travel: Up to 25% ** Job ID:** 1875

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Shape the technical strategy and multi-year roadmap for operational excellence and production readiness across development, pre-production, and production environments.
Define and govern the New Service Introduction framework, including mandatory architecture, security, resilience, capacity, observability, supportability, and release-readiness reviews before services enter production.
Establish organization-wide service ownership standards covering service catalog records, accountable owners, dependency maps, runbooks, support models, escalation paths, recovery objectives, and on-call readiness.
Lead the architecture and evolution of the shared observability platform, establishing consistent standards for logs, metrics, distributed traces, and profiles across production systems.
Define standards for dashboards, alert policies, synthetic monitoring, telemetry quality, retention, sampling, cardinality, and cost controls.
Own the reliability governance model for production services, including SLIs, SLOs, error budgets, and escalation mechanisms.
Connect service-health signals to customer and business impact, enabling early anomaly detection, service-degradation prevention, and rapid isolation of end-user-impacting events.
Advance incident-management maturity through consistent severity classification, incident command, stakeholder and executive communications, automated evidence collection, and coordinated response to high-severity incidents.
Establish blameless post-incident review practices, ensure remediation actions are tracked to completion, and drive systemic fixes for recurring failure modes.
Lead operational capacity and efficiency management, including demand forecasting, cloud and Kubernetes capacity, performance testing, scaling thresholds, headroom policies, resource rightsizing, and capacity-risk reviews.
Design and govern AI Ops capabilities for event correlation, alert-noise reduction, predictive detection, probable root-cause analysis, autonomous triage, assisted remediation, and controlled self-healing.
Deliver secure AI-agent workflows across observability platforms, service catalog, Jira, Confluence, source control, and CI/CD.
Improve on-call effectiveness through sustainable rotation design, operational-readiness standards, escalation policies, diagnostic automation, alert-quality management, and reliable follow-the-sun handoffs.
Provide hands-on technical leadership during major incidents, complex reliability investigations, architectural reviews, resilience exercises, and critical service launches.
Use operational data, incident trends, service-level performance, capacity signals, change outcomes, and automation effectiveness to prioritize continuous improvement.