Software Development Test Engineer (SDET), AI & Automation

Description

Keeper Security is hiring a Senior Software Development Engineer in Test (SDET), AI & Automation to join our AI team and help build a modern, automated approach to quality across AI-powered products and services. This is a 100% remote position, with an opportunity to work a hybrid schedule for candidates based in the El Dorado Hills, CA or Chicago, IL metro areas.

Keeper’s cybersecurity software is trusted by millions of people and thousands of organizations globally. Keeper is published in 23 languages and sold in over 150 countries. Join one of the fastest-growing cybersecurity companies and help establish scalable quality engineering practices for AI-driven systems operating in security-sensitive environments.

About Keeper

Keeper Security is one of the fastest-growing cybersecurity software companies that protects thousands of organizations and millions of people in over 150 countries. Keeper is a pioneer of zero-knowledge and zero-trust security built for any IT environment. Its core offering, KeeperPAM®, is an AI-enabled, cloud-native platform that protects all users, devices and infrastructure from cyber attacks. Recognized for its innovation in the Gartner Magic Quadrant for Privileged Access Management (PAM), Keeper secures passwords and passkeys, infrastructure secrets, remote connections and endpoints with role-based enforcement policies, least privilege and just-in-time access. Learn why Keeper is trusted by leading organizations to defend against modern adversaries at KeeperSecurity.com.

About the Job

As a Senior SDET, AI & Automation, you will serve as an embedded quality engineering partner within Keeper’s AI team. You will work alongside engineers and Product throughout design and development to define test strategy, establish acceptance criteria and build automated coverage as AI-powered capabilities are being developed.

This role requires experience testing both traditional deterministic software systems and non-deterministic AI/ML systems. Traditional application behavior can often be validated against consistent expected outputs, while AI models may produce different but acceptable results across models, prompts and inputs. You will help design testing approaches that account for this variability while still establishing measurable quality, reliability and safety standards. You will build automation frameworks, develop deterministic and probabilistic validation strategies, integrate testing into CI/CD workflows and help Keeper move toward continuous quality engineering rather than end-of-cycle QA.

Responsibilities

• Partner directly with AI engineers, Software Engineers and Product Managers during design and development to define test strategy, acceptance criteria and automation requirements
• Design and maintain automated testing frameworks for AI-powered applications, backend services, APIs and supporting software systems
• Develop testing strategies for both deterministic software behavior and non-deterministic AI/ML outputs
• Define measurable evaluation criteria, tolerances and quality thresholds for AI model and LLM-driven functionality
• Build automated regression, integration, API and end-to-end coverage alongside feature development
• Develop test harnesses and evaluation workflows for prompts, models, agents, tool-calling workflows and AI-integrated applications
• Validate AI behavior across different models, prompts, inputs and system configurations
• Develop automated test utilities using languages such as Python, Java, JavaScript/TypeScript, Rust or similar technologies
• Integrate automated validation and quality gates into CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI or similar platforms
• Build and maintain test datasets, mocks, fixtures and synthetic data required for repeatable AI and software validation
• Analyze model and application failures to distinguish defects, expected model variability, data issues and test instability
• Evaluate AI outputs for reliability, consistency, safety and alignment with expected product behavior
• Work with engineering teams to improve application testability, observability and diagnostic capabilities
• Track test cases, execution results and coverage using TestRail or similar test management platforms
• Analyze logs, telemetry and system behavior to identify root causes and produce clear, reproducible defect reports
• Reduce flaky tests and improve the speed, stability and maintainability of au…