Senior Security Engineer, Red Team

What matters most

• AI System Security Assessment

  A primary focus of this role is red teaming AI-specific infrastructure, including internal copilots, agentic workflows, and MLOps environments. You must be prepared to demonstrate how you identify vulnerabilities in AI models and pipelines beyond traditional application security.

• End-to-End Red Team Operations

  You are expected to manage full-lifecycle red team operations, from initial planning and tool development to execution and reporting. This requires balancing technical offensive tradecraft with the ability to translate complex findings into actionable engineering improvements for defensive teams.

• Intelligence-Led Purple Teaming

  The role requires integrating external threat intelligence and adversary behavior trends into Purple Team exercises. You must be able to partner effectively with Blue Teams to validate detection and response capabilities against realistic, high-impact scenarios.

• Offensive Tooling and Automation

  You must possess hands-on experience in developing custom offensive tooling, including command-and-control infrastructure, implants, and automation scripts. Leveraging AI to enhance these research and operational workflows is a core expectation.

Likely challenges to think through

• Securing Rapidly Evolving AI Architectures

  Assessing agentic workflows and MLOps environments presents a challenge due to the lack of standardized security patterns. Prepare by researching emerging AI-specific attack vectors and demonstrating how you adapt traditional security testing methodologies to non-deterministic AI systems.

• Bridging the Gap Between Offensive Findings and Engineering Remediation

  A common difficulty in red teaming is ensuring that findings lead to actual architectural hardening rather than just temporary patches. Prepare by focusing on how you communicate risk to leadership and collaborate with platform teams to prioritize long-term security improvements.

• Navigating Complex Cloud and CI/CD Environments

  The role requires deep expertise in assessing CI/CD pipelines and cloud infrastructure. The challenge lies in identifying subtle privilege escalation paths within these interconnected systems. Prepare by reviewing common misconfigurations in modern build pipelines and cloud identity management.

Topics to prepare

• Adversarial AI Security

  Focus on the security of AI systems, specifically targeting vulnerabilities in copilots, agentic workflows, and MLOps environments. Exercise: Review the OWASP Top 10 for LLMs and map these to potential attack vectors within a CI/CD pipeline. This is critical as the role explicitly requires red teaming AI-enabled products.

• Intelligence-Led Purple Teaming

  Understand how to integrate external threat intelligence into adversary simulation exercises. Exercise: Take a recent public security incident report and design a Purple Team exercise plan that tests specific detection and response capabilities against the tactics described. This aligns with the requirement to translate findings into actionable engineering improvements.

• Cloud and CI/CD Security Architecture

  Deepen knowledge of cloud identity, container security, and the security of build pipelines. Exercise: Analyze common privilege escalation paths in cloud environments and identify how misconfigured CI/CD secrets or build workflows can lead to full environment compromise. This is essential for assessing Grab's complex infrastructure.

Skills to revise

• Offensive Tooling Development

  Develop custom command-and-control (C2) tooling, implants, or loaders. Exercise: Build a small-scale, modular C2 agent in a language like Go or Rust that demonstrates basic beaconing and task execution. This directly addresses the requirement for hands-on experience in developing offensive automation.

• CI/CD Pipeline Assessment

  Practice auditing build pipelines and artifact repositories for security flaws. Exercise: Create a mock CI/CD pipeline configuration and perform a security audit to identify insecure secrets management, lack of build integrity, or excessive permissions. This is a core technical requirement for the role.

• AI-Assisted Security Research

  Leverage AI tools to enhance research, automation, and reporting. Exercise: Use an AI coding assistant to automate the parsing of large log files or to generate boilerplate code for a security research script. This demonstrates the ability to scale offensive security work as requested in the job description.

• Prepare an AI security assessment case study

  Develop a brief presentation or document detailing a past engagement where you assessed an AI system, such as an LLM-based copilot or MLOps pipeline. Highlight the specific threat vectors you tested, such as prompt injection or model poisoning, and how you translated those findings into actionable security hardening for engineering teams.

• Demonstrate offensive tooling development

  Prepare a portfolio or code repository samples showcasing your ability to build custom offensive tooling. Focus on command-and-control (C2) infrastructure, custom implants, or automation scripts that streamline red team operations, emphasizing your proficiency in languages like Go, Python, or C++.

• Map CI/CD and cloud attack surfaces

  Create a technical walkthrough of a complex cloud environment or CI/CD pipeline you have previously compromised or audited. Explain your methodology for identifying privilege escalation paths, secrets management vulnerabilities, and container security gaps, and how you communicated these risks to stakeholders.

• Formulate threat intelligence-led exercise plans

  Draft a sample plan for a Purple Team exercise that incorporates current threat intelligence. Explain how you would select specific adversary behaviors relevant to Grab's industry, coordinate with the Blue Team to test detection coverage, and measure the effectiveness of the response.

• Prepare strategic questions for the interview

  Develop thoughtful questions regarding Grab's current red team roadmap, such as how the team balances AI-driven automation with manual research, or how the team prioritizes findings when working with diverse engineering stakeholders across the Southeast Asian market.