Software Engineering SMTS

Salesforce•San Francisco, CA

About The Position

Salesforce is seeking a Senior Member of Technical Staff (SMTS) Software Engineer to design, build, automate, and operate reliable, secure, and scalable cloud infrastructure and production platforms. The role works across software engineering, infrastructure, security, and product teams to improve service availability, deployment reliability, operational efficiency, and developer productivity. It is an engineering-focused development position that emphasizes software and automation over repetitive manual operations, systematic investigation of infrastructure and production issues, and understanding system behavior across application and infrastructure layers. The engineer is expected to use modern engineering practices, including AI-assisted development, to improve reliability, automation, and delivery effectiveness. The position covers the full engineering lifecycle, from requirements refinement and design through development, testing, deployment, monitoring, incident response, and continuous improvement. Work includes developing software and automation, managing cloud resources through Infrastructure as Code, improving CI/CD, operating containerized workloads, building observability, troubleshooting production systems, and applying security and reliability controls. The engineer delivers well-scoped improvements independently after alignment on the overall technical plan and architecture, and seeks guidance on decisions with broader architectural, security, or product impact. Salesforce describes its benefits as including time-off programs, medical, dental and vision coverage, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. The typical annual base salary range is $148,500–$223,900, with a range of $178,900–$246,000 in select cities in the San Francisco and New York City metropolitan areas. These ranges are base salary only and exclude bonus, equity, and benefits, as applicable.

Requirements

At least 6 years of experience designing and building production software, including 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, with the ability to 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, using AI tools such as Claude Code, GitHub Copilot, Codex, or Cursor to work faster, build fluency across the stack, and contribute beyond a core specialty.
Experience with Infrastructure as Code and configuration automation 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 such as Postgres, HBase, or Oracle and database cluster technologies such as Oracle RAC.
Experience with Linux systems and working knowledge of operating systems, networking, processes, 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 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 ability to collaborate across engineering and operational teams.

Nice To Haves

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, including writing precise, structured prompts and building system context for reliable, secure, production-ready AI outputs.
Experience with Site Reliability Engineering 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 to 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, including scalability, fault tolerance, service communication, networking, availability, resiliency, and capacity management.
Build and improve observability using metrics, logs, traces, dashboards, alerts, and health checks to provide actionable visibility into production systems.
Diagnose production incidents using telemetry and system-level analysis, identify contributing factors and root causes, and implement improvements to prevent recurrence.
Identify repetitive operational activities and convert them into safe, testable, and maintainable automation.
Participate in production support and incident response, 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 decisions have broader architectural, security, or product impact.
Apply security and reliability practices throughout infrastructure and software delivery, including access controls, secrets management, change validation, testing, rollback, and deployment safeguards.
Participate in the full engineering lifecycle, including requirements refinement, design, development, code review, testing, deployment, monitoring, production support, and operational improvement.
Use AI-assisted development tools responsibly to accelerate automation and engineering work, improve code and configuration quality, assist troubleshooting and testing, and increase team effectiveness while applying appropriate security and quality controls.
Collaborate with software engineers, infrastructure teams, security teams, and other cross-functional partners; communicate technical and operational decisions clearly and contribute to engineering documentation and runbooks.