Senior Site Reliability Engineer

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. 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. The Experience 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. What You'll Actually Be Doing: 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. You're Our Person If You Have: 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 Advan