Software Engineering SMTS

Salesforce•San Francisco, CA

1d•$148,500 - $246,000•Onsite

About The Position

This role sits within our Quality Engineering organization, at the intersection of quality architecture, test automation, and performance optimization. You'll partner closely with developers, software architects, and product managers to ensure every release meets Salesforce's standards for quality, low latency, and enterprise-grade scalability. We're looking for a software engineer who designs scalable automation frameworks, drives shift-left quality methodologies, and tunes complex distributed systems to withstand enterprise-grade load — not someone who just tests code.

Requirements

5–8+ years of experience in software development, test automation framework architecture, and performance engineering in enterprise-grade SaaS or distributed systems.
Coding skills in Java, Python, or TypeScript/Node.js.
Hands-on experience with UI and API testing frameworks (e.g., Playwright, Selenium WebDriver, Cypress, RestAssured, Postman).
Experience with load/performance tools (k6, JMeter, Gatling, or Locust) and observability tools (Grafana, Prometheus, New Relic, or Splunk), plus familiarity with AWS (or GCP/Azure), Docker, Kubernetes, and CI/CD tooling.

Nice To Haves

Prior experience with Apex, Lightning Web Components (LWC), Data Cloud, or Agentforce ecosystem testing.
Used AI coding/testing assistants (e.g., Claude Code, GitHub Copilot, Cursor) or tested LLM/RAG pipelines.
Strong understanding of SQL query tuning, indexing strategies, and relational/NoSQL database performance.

Responsibilities

Establish test strategies, customer usage modeling, and quality gates early in the design and development phases, and define critical quality metrics (test coverage, defect density, MTTR) to raise team standards.
Lead cross-functional triage, identify architectural flaw patterns, and drive systemic fixes across the Agentic Development Life Cycle (ADLC).
Design, build, and maintain scalable automation frameworks for UI, REST/gRPC APIs, and microservices, and integrate automated regression, contract, and sanity suites into CI/CD pipelines (e.g., Jenkins, Harness, GitHub) using containerized test environments (Docker/Kubernetes).
Architect and execute enterprise-scale performance scenarios (JMeter, k6, Locust) to simulate high-concurrency workloads, analyzing CPU, memory, database, and network metrics to uncover bottlenecks — then work with platform architects to set SLA/SLOs and optimize heap/GC settings and query execution plans.