Software Engineer – AI Quality & Test Automation | Security - Python | Golang | Test Automation | AI/ML Testing | Performance Testing | Security Testing | LLM APIs | Agentic Workflows | LangChain | LangSmith | Contract Testing | Playwright (4 - 8 Years)

About this role

Role Summary

Splunk Enterprise Security (ES) is a sophisticated SIEM built atop Splunk’s data platform with lots of moving parts. We help thousands of customers as they identify and protect their assets from cyber threats, all over the world. On any given day, our software needs to handle thousands of users, petabytes of data, and unique usage patterns across different deployment topologies. 

Meet the Team

Are you ready to shape the future of AI-powered quality? We are seeking a software engineer to build test infrastructure for our AI-agentic Security Operations Center (SOC)! Together, we define the strategic direction that secures the absolute reliability and performance of our most complex systems. We work to mentor world-class engineering talent, drive the adoption of groundbreaking benchmarking, and catalyze the evolution of our quality ecosystems. Join us as we work to transform high-level challenges into scalable, high-impact solutions. 
 

Your Impact

This role is designed for an engineer who translates quality challenges into technical solutions, driving engineering efficiency through evaluation strategies and implementation. In this role, you will collaborate with team members and implement benchmarking protocols that support our technical performance goals. Responsibilities include:

• Technical Contribution: Contribute to the development of PQ foundations and evaluation frameworks, ensuring assessment capabilities for AI agents, workflows, and system processes.
• Technical Collaboration & Support: Support the team by collaborating with members, participating in code and design reviews, and assisting in the validation of technical hypotheses.

• Experimental Execution: Implement the lifecycle of proof-of-concepts (POCs), adhering to standards for experimentation and the validation of testing paradigms.

• Quality Ecosystem Maintenance: Ensure that testing processes deliver reliable, high-quality outcomes, maintaining alignment with team ...