Senior Site Reliability Engineer, Salesforce

• 5+ years of experience in systems engineering and software engineering for large-scale, internet-facing services
• Hands-on expertise with containerized architectures (Docker, Kubernetes) and orchestration platforms
• Strong knowledge of distributed systems and Linux/Unix internals, with experience tuning performance and troubleshooting at scale
• Familiarity with large-scale internet service architectures (DNS, HTTP, Load Balancing, caching, etc.)
• Proven proficiency in Python and Go (GoLang) with strong software engineering practices (testing, code review, CI/CD)
• Production experience building and operating observability platforms (Grafana, Prometheus, ELK, Splunk, Datadog, or similar)
• Solid background in incident management, including on-call participation, root cause analysis, and postmortem practices
• Strong understanding of SRE principles: SLIs/SLOs, error budgets, toil reduction, blameless culture, and capacity planning
• Hands-on experience with workflow/orchestration engines (Temporal, Airflow, Argo Workflows, or similar) for building durable automation pipelines
• Experience applying AI/ML to operations — including anomaly detection, predictive analysis, LLM-based automation, and prompt engineering to build intelligent operational agents and workflows
• Excellent communication skills with demonstrated ability to lead during high-pressure incidents, present technical designs to leadership, and mentor junior engineers
• Track record of mentoring and technically coaching other engineers
• Ability to work in a 24/7 global operations model, managing multiple priorities under time-sensitive conditions
• Growth mindset with curiosity to explore new technologies and drive continuous improvement
• A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty
• Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows
• Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready
• A related technical degree required

Desirable

• Salesforce is seeking a senior engineering candidate to join the Site Reliability organization in San Francisco
• Working closely with counterparts in the Infrastructure and R&D organizations, this organization provides a global team of engineers monitoring cloud service availability and ready to swiftly repair any service-impacting issues
• Five days a week, 24 hours a day, in a follow-the-sun model with weekend oncall, the Site Reliability team keeps the Salesforce cloud and our customers protected
• As an SRE, you will be a technical leader of the team driving Salesforce’s operational resilience by engineering solutions that blend automation, observability, and AI-powered platforms
• You will not only respond to incidents but proactively design systems that prevent them, applying software engineering principles to operations to reduce toil and improve reliability at scale
• By leveraging cutting-edge software engineering practices within SRE function and AI-driven insights, you will help transform how services are built, monitored, and operated — ensuring that Salesforce delivers always-on, high-performance experiences to customers worldwide
• Build and run reliable, scalable, and efficient systems by applying software engineering principles to operations
• Our mission is to ensure services are highly available, performant, and resilient — while continuously improving the balance between operational work and engineering innovation
• Reliability as the Priority: Ensure that systems meet defined Service Level Indicators (SLIs) and Service Level Objectives (SLOs), using error budgets to guide engineering and release decisions
• Engineering for Operations: Apply software engineering practices — automation, monitoring, self-healing systems — to eliminate toil and improve operational efficiency
• Incident Management: Lead the coordinated response to incidents as an Incident Commander, drive fast recovery (low TTR), and ensure lasting improvements through blameless postmortems
• Continuous Improvement: Identify and remove sources of toil, enhance observability, and optimize systems to reduce Time to Detect (TTD) and Time to Restore (TTR)
• Collaboration with Development: Partner with product and engineering teams early in the lifecycle to design, build, and operate systems that are reliable by default
• Long-Term Focus: Leverage AI-driven automation to eliminate manual workflows, enabling the team to focus on complex problem-solving and strategic innovation while reducing operational overhead to less than 20% of capacity
• Lead incident detection, response, and resolution—driving root cause analysis, postmortems, and proactive measures to ensure high uptime, rapid recovery, and prevention of future issues
• Lead post-incident reviews, drive systemic fixes through corrective actions, and ensure customer-facing services maintain peak performance and reliability
• Understanding of AI/ML concepts applied to operations (e.g., anomaly detection, predictive analysis)
• Independently drive the design and implementation of complex automation platforms, self-healing systems, and AI-powered operational tooling using durable workflow engines (Temporal, Airflow, Argo Workflows)
• Architect and build production-grade observability solutions — monitoring, logging, alerting, and tracing systems — that enable proactive detection and autonomous remediation
• Design and implement AI/ML-powered operations tools including anomaly detection systems, predictive analysis pipelines, intelligent runbook automation, and prompt-engineered operational agents (MCP-based)
• Drive optimization of system performance, reliability, and cost-effectiveness through proactive monitoring and tuning
• Ensuring that work carried out by the Site Reliability team is executed in such a way as to comply with the company’s internal compliance policy and directives
• Identifying opportunities and driving the creation of comprehensive technical epics that include well-defined problem statements, detailed project and implementation documentation, and clearly measurable business outcomes aligned with team objectives
• Provide technical coaching to junior team members through pair programming, design reviews, and code reviews — helping grow their skills and knowledge
• Collaborate with engineering and product teams to define and uphold SLAs/SLOs, driving improvements in service reliability and customer experience
• Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code
• Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale
• Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance
• Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably