Software Engineering SMTS
Salesforce · California, San Francisco, United States
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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.
Role DescriptionWe are looking for a Software Engineer, Senior Member of Technical Staff (SMTS), to design, build, automate, and operate reliable, secure, and scalable cloud infrastructure and production platforms. You will work with software engineers, infrastructure teams, security partners, and product teams. Your work will improve service availability, deployment reliability, operational efficiency, and developer productivity.
The ideal candidate has strong experience with software development, public cloud and cloud-native technologies, infrastructure automation, CI/CD, observability, and production operations. They also take a practical, AI-first approach to their work. This is an engineering-focused development role. We expect automation and software development to replace repetitive manual operations.
You approach ambiguous infrastructure and production problems methodically. You understand system behavior across application and infrastructure layers. You use modern engineering practices, including AI-assisted development, to improve reliability, automation, and delivery effectiveness.
Required Qualifications- 6+ years of experience designing and building production software. This includes full-stack applications, backend services, developer tools, or internal platforms.
- Strong programming skills and experience working across frontend, backend, APIs, data stores, and distributed system boundaries.
- Experience designing, deploying, or operating production workloads on public cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Development or automation experience using Core Java, Python, Go, or another programming language. You can build maintainable, production-quality automation, tools, services, or APIs.
- Strong understanding of cloud infrastructure concepts, including compute, storage, networking, load balancing, identity and access management, security, high availability, and disaster recovery.
- Experience with containerization and cloud-native technologies such as Docker, Kubernetes, or comparable container orchestration platforms.
- A demonstrated AI-first approach to engineering. You use AI tools (for example, Claude Code, GitHub Copilot, Codex, or Cursor) to work faster, build fluency across the stack, and contribute beyond your core specialty.
- Experience with Infrastructure as Code and configuration automation using tools such as Terraform, CloudFormation, Ansible, Helm, or comparable tooling.
- Experience building, maintaining, or improving CI/CD pipelines and automated build, test, deployment, and release workflows.
- Familiarity with databases (Postgres, HBase, Oracle, or similar) and database cluster technologies such as Oracle RAC (Real Application Clusters).
- Experience with Linux systems and working knowledge of operating systems, networking, process, memory, storage, and performance fundamentals.
- Experience with production observability using logs, metrics, traces, dashboards, and alerting platforms.
- Understanding of distributed-system and reliability concepts, including scalability, fault tolerance, resiliency, service dependencies, capacity, availability, and performance.
- Experience troubleshooting complex production issues across application, infrastructure, networking, and cloud layers.
- Experience working across the software and infrastructure delivery lifecycle: design, implementation, testing, deployment, monitoring, incident response, and continuous improvement.
- Experience using AI development tools or techniques for automation, software development, troubleshooting, testing, debugging, and documentation.
- Strong analytical and problem-solving skills, including the ability to diagnose complex infrastructure and production problems systematically.
- Effective written and verbal communication skills and the ability to collaborate across engineering and operational teams.
Preferred Qualifications- Experience operating large-scale distributed systems or high-availability production platforms.
- Experience with Kubernetes ecosystem technologies, service discovery, ingress, service mesh, autoscaling, or container platform engineering.
- Experience building internal developer platforms, self-service infrastructure, deployment frameworks, or operational automation.
- Experience developing backend services, APIs, command-line tools, controllers, operators, or automation frameworks.
- Advanced prompt engineering skills. You can write precise, structured prompts and build the system context that makes AI outputs reliable, secure, and production-ready.
- Experience with Site Reliability Engineering (SRE) practices, including service level objectives (SLOs), service level indicators (SLIs), error budgets, capacity management, and incident management.
- Experience with cloud security, IAM, secrets management, vulnerability management, policy enforcement, and infrastructure security controls.
- Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, Splunk, or comparable platforms.
- Experience with databases, caching systems, messaging platforms, or distributed data infrastructure.
- Familiarity with multi-region, multi-cloud, disaster recovery, or large-scale cloud migration architectures.
- Familiarity with Agile development practices and iterative release cycles.
Responsibilities- Design, build, automate, and operate secure, reliable, and scalable cloud infrastructure and production platforms.
- Independently deliver well-scoped cloud, DevOps, reliability, and automation improvements after alignment on the overall technical plan and architecture.
- Develop software, automation frameworks, scripts, APIs, and engineering tools in Python, Go, Java, or comparable languages. These reduce manual operations and improve platform reliability.
- Build and maintain Infrastructure as Code for repeatable provisioning, configuration, deployment, and lifecycle management of cloud resources.
- Develop and improve CI/CD pipelines that automate build, testing, security validation, deployment, rollback, and release processes.
- Deploy and operate containerized workloads using Kubernetes or comparable cloud-native platforms.
- Apply cloud and distributed-systems concepts such as scalability, fault tolerance, service communication, networking, availability, resiliency, and capacity management.
- Build and improve observability using metrics, logs, traces, dashboards, alerts, and health checks. These give the team actionable visibility into production systems.
- Diagnose production incidents using telemetry and system-level analysis. Identify contributing factors and root causes, and implement improvements that prevent recurrence.
- Identify repetitive operational activities and convert them into safe, testable, and maintainable automation.
- Participate in production support and incident response. This includes troubleshooting complex issues across application, cloud, Kubernetes, networking, storage, and infrastructure layers.
- Contribute to infrastructure and platform design discussions, identify implementation and operational tradeoffs, and seek guidance when decisi