Software Engineering Lead/Principal - AI Tooling & Automation

Salesforce•San Francisco, CA

5d•$172,500 - $344,700

About The Position

As a Lead/Principal Engineer in Quality & Performance Excellence, you will operate at the intersection of quality architecture, intelligent test automation, performance engineering, and engineering excellence. You will drive AI-native engineering practices by establishing and scaling industry-leading approaches to quality, performance, reliability, and delivery across the organization. You will enable engineering teams to deliver faster, safer, and more reliable customer outcomes through engineering standards, intelligent automation, quality governance, and continuous innovation. In this role, you will work across engineering and product to shape how teams design, build, validate, and deliver software. You will leverage AI, intelligent automation, engineering rigor, and data-driven insights to accelerate delivery while continuously raising the bar for quality, reliability, scalability, and performance.

Requirements

12+ years of experience in software development, test automation framework architecture, and performance engineering in enterprise-grade SaaS or distributed systems environments.
Strong coding skills in at least one modern language: Java, Python, or TypeScript/Node.js.
Hands-on experience with modern UI and API testing frameworks (e.g., Playwright, Selenium WebDriver, Cypress, RestAssured, Postman).
Deep expertise with load and performance tools such as K6, JMeter, or Locust, alongside APM and observability tools like Grafana, Prometheus, New Relic, or Splunk.
Familiarity with AWS (or GCP/Azure), containerization (Docker), orchestration (Kubernetes), and CI/CD tools.
BS or MS in Computer Science, Software Engineering, or equivalent practical experience.

Nice To Haves

Prior experience with Apex, Lightning Web Components (LWC), Data Cloud, or Agentforce ecosystem testing.
Experience utilizing AI coding/testing assistants (e.g., Claude Code, GitHub Copilot, Cursor) or testing LLM/RAG pipelines.
Strong understanding of SQL query tuning, indexing strategies, and relational/NoSQL database performance behavior.
Ability to support and resolve complex customer escalations through deep technical debugging, root-cause analysis, and effective cross-functional collaboration.

Responsibilities

Drive quality strategy and engineering excellence by establishing customer usage models, comprehensive test strategies, and quality gates early in the architecture and development lifecycle.
Design and evolve resilient technical architectures that address complex functional, performance, scalability, reliability, and fault-tolerance challenges across systems.
Lead complex, cross-functional technical triage by identifying systemic and architectural flaw patterns and driving durable fixes across the Agentic Development Lifecycle (ADLC).
Architect and build scalable quality engineering frameworks spanning UI, REST/gRPC APIs, microservices, and end-to-end workflows using Java, Python, and TypeScript/JavaScript.
Embed continuous quality into the software delivery lifecycle by integrating automated regression, monitoring, alerting, and sanity testing into modern CI/CD pipelines and developer workflows.
Architect scalable, production-like test environments using containerization and orchestration technologies such as Docker and Kubernetes, along with synthetic test-data generation capabilities.
Define and execute enterprise-scale performance strategies that model high-concurrency, customer workloads using JMeter, k6, and custom performance harnesses.
Lead deep-dive performance analysis across distributed systems by analyzing CPU, memory, database, network, and execution-profiling metrics to identify bottlenecks and systemic performance issues across microservices and multi-tenant platform
Leverage AI and intelligent automation to transform quality and performance engineering from reactive validation into proactive, continuous engineering intelligence across the development lifecycle.