Who we are
Checkmarx is the leader in application security, ensuring that enterprises worldwide can secure their application development from code to cloud. Our consolidated platform and services address the needs of enterprises by improving security and reducing TCO, while simultaneously building trust between AppSec, developers, and CISOs. At Checkmarx, we believe it's not just about finding risk, but remediating it across the entire application footprint and software supply chain with one seamless process for all relevant stakeholders. We are honored to serve more than 1,800 customers, including 40 percent of all Fortune 100 companies, such as Siemens, Airbus, Salesforce, Stellantis, Adidas, Walmart, and Sanofi.
About the role
We're looking for a Senior AI Engineer to join one of our R&D teams building the AI-driven core of our product. Beyond building, we want someone who tracks the AI landscape closely: new models, frameworks, and agent architectures as they emerge; and who can turn that knowledge into real recommendations: evaluating whether a new tool, model, or technique is worth adopting, and helping the team decide what to build vs. buy vs. integrate.
The specific product area may evolve with our roadmap, but the core of the job stays constant: AI-driven features, applied judgment about the AI tooling landscape, and cloud-native architecture in Go.
What you'll do
- Design, build, and own LLM-powered pipelines and agent-based systems that find, triage, and remediate security risk in production, at scale
- Design retrieval and context strategies (RAG, tool use, memory) that ground model behavior in our product's data and domain
- Define and run evaluations for model/agent quality and use them to drive iteration and model selection
- Evaluate emerging AI tools, models, and frameworks against our product needs, producing clear build-vs-buy-vs-integrate recommendations for the team and leadership
- Stay ahead of the AI landscape: new LLM releases, agent architectures, orchestration frameworks, and techniques and translate relevant developments into concrete engineering proposals
- Own the reliability of AI features in production: guardrails, structured output validation, fallback behavior, and observability tuned for LLM-based systems
- Participate in design discussions and code reviews, bringing an AI-informed perspective to architecture decisions
