Senior/Lead Quality Engineer - AI Platform

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Software Engineering About Salesforce: We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place. About the organization: Einstein products & platform democratize AI and transform the way our Salesforce Ohana builds trusted machine learning and AI products - in days instead of months. It augments the Salesforce Platform with the ability to easily create, deploy, and manage Generative AI and Predictive AI applications across all clouds. We achieve this vision by providing unified, configuration-driven, and fully orchestrated machine learning APIs, customer-facing declarative interfaces and various microservices for the entire machine learning lifecycle including Data, Training, Predictions/scoring, Orchestration, Model Management, Model Storage, Experimentation etc. About the team: Join the AI Platform Quality Engineering team, and become a specialist on Salesforce's AI Platform! You'll get to work with latest technology in the AI space (including generative AI), and collaborate with the team and cloud to identify and run quality initiatives to support massive scale planned for this year. We are a small, friendly team that has been working together for 3+ years - outside of quality, we focus on volunteering and shared interests such as gardening! Job description summary: As a Quality Engineer for the Salesforce AI platform,

you will be responsible for leading the designing, development and execution of comprehensive test plans/quality strategies with emphasis on automation. You will collaborate closely with engineering and product teams actively contributing to the quality of new features while ensuring successful execution of regression tests in various environments. You will be working in a fast-paced environment and will be expected to understand complex backend AI flow systems/dependencies, identifying potential risks, and strategically plan and execute comprehensive testing efforts to ensure the stability, reliability and quality of the feature and release. Skills needed for role: A related 4-year technical degree required Strategic and Tactical leadership:

Experience leading analysis of coverage gaps and writing E2E and integration tests, partnering with teams to drive service level test coverage Technical Expertise:

Technical excellence and leadership required in identifying, developing and maintaining tests involving API, databases and web UI tests. Strong knowledge of automation tools and continuous automation systems. Cross Team Collaboration:

Ability to work with stakeholders (internal customers) from multiple organizations that leverage our central platform for custom applications. Collaborate with them to determine key shared usage patterns to prioritize test coverage of, as well as provide guidance to these customers on how to proactively test their application’s integration with the platform. Strong knowledge and experience of Java/Python/Selenium/CD pipeline configurations Excellent communication skills:

Experience generating reports, presenting and holding the quality bar for new features & release Experience working in Global teams:

Ability to work and partner with peers and other partner teams across timezones. 6+ years of QE experience and 2+ years of experience leading projects and partnering/leading junior members of the team. Nice to have skillsets (or skillsets expect to develop in this role): Understanding of LLM Model hosting with Sagemaker Understanding LLM Finetuning and Inferencing Testing integrations with Cohere, Anthropic, Dolly, Google, etc. as well as internal models Sagemaker GroundTruth integration for data labeling, Bedrock for model serving Experience with Azure Open AI What the team does: Center of Excellence for Quality:

driving excellence through automation, tools, and streamlined processes (evangelizing shift left methodologies). Building/enhancing test frameworks/tooling:

for the easy creation and maintenance of integration and end-to-end tests. Proactively drive quality:

by monitoring and reporting of test stability metrics for pre-prod & Prod environments through dashboards, triaging and filing and follow up of bugs. CUJs (Critical User Journey test):

for cross-cloud integration and apps Configure tooling/runners:

for automated tests, used by scrum teams Configure various test environments:

such as setting up core test env hawking test env connectivity Define and measures Quality metrics:

(code coverage, endpoint coverage, service coverage, etc.) goals are met for the ML foundations Automation strategies and framework:

support for all kinds of testing Identify risks:

with releases, deployments, etc.

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