Staff Security Engineer, Zipline

• Our ideal candidate works well in startup environments, wears many hats, and collaborates across engineering disciplines. You’ll join a small, high-ownership security team with significant influence over how we scale
• 8+ years of experience designing, building, and operating security controls for large-scale production systems (application, cloud, and infrastructure security)
• Strong security engineering chops with evidence you can reduce risk in production systems (not just talk about it)
• Hands-on ability to write and ship code/tools in Python, Go, or similar (you’re expected to build, not just review)
• Practical experience securing microservice architectures and modern cloud stacks (containers/Kubernetes, IAM, CI/CD, secrets, logging)
• Comfort operating as a technical leader without authority: you can persuade, teach, and unblock - not police
• A skeptical mindset: you naturally ask “what’s the failure mode?” and “how will this be abused?” before shipping changes
• Familiarity with the security failure modes of LLM-enabled systems (or the willingness to learn fast), including risks called out by OWASP such as prompt injection, insecure output handling, insecure plugin design, and excessive agency
• We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply

Desirable

• Zipline builds and operates fleets of delivery drones to get medicine to those who need it, fast, regardless of where they live. To power this, the software team is building out the long term scalable solutions to expand rapidly while empowering our world class distribution centers to serve their customers as fast as possible
• Zipline’s security problems aren’t “website got pwned” problems (though those exist too). They’re “real-world autonomy + robotics + global operations + cloud software + regulated/health-adjacent workflows” problems
• You’ll partner deeply with software, infrastructure, and (where relevant) embedded/autonomy teams to reduce real risk in real systems. We have a large attack surface
• Engineering teams are increasingly adopting LLM copilots and agentic tools to move faster. That’s useful, until an “assistant” becomes an unmonitored automation path to secrets, sensitive data, or privileged actions. (Think: “obedient intern with production credentials.”)
• Industry guidance is converging on practical frameworks like the NIST AI Risk Management Framework (including a profile for generative AI) and the OWASP Top 10 for LLM Applications, which explicitly calls out risks like prompt injection, insecure plugin design, and excessive agency
• In this role, you’ll help Zipline safely leverage these tools while containing them so they don’t quietly “rewrite the threat model”
• Own security outcomes for critical parts of Zipline’s application and cloud ecosystem (not by writing policy docs that no one reads, but by shipping controls and enabling teams)
• Partner with engineering teams on secure architecture, threat modeling, and design reviews for services that must be correct, reliable, and defensible under real-world operational pressure
• Help us build and scale a pragmatic secure SDLC – CI/CD hardening, dependency/supply-chain controls, secrets management, and code review patterns that don’t slow teams down
• Improve cloud security posture end-to-end: IAM and least privilege, network/service-to-service trust, key management, logging/telemetry, runtime detection, and incident-ready auditability
• Drive vulnerability management that actually closes risk: triage, exploitability analysis, remediation partnerships, and verification
• Help build and exercise incident response: playbooks, tabletop exercises, logging requirements, and “know it happened / know what changed” operational discipline
• Support data classification and access control models aligned to how Zipline operates (including partner/customer interfaces and global operations)
• Support external penetration tests and turn results into durable improvements, not whack‑a‑mole patches
• Contribute to security compliance efforts (e.g., SOC 2 / ISO 27001) in a way that strengthens engineering
• Secure AI-assisted and agentic engineering workflows (this is explicitly part of the job):
• Define safe patterns for copilots/LLM tools used in development and ops
• Implement guardrails for sensitive data exposure and output handling
• Prevent “agentic overreach” (over‑privileged tools, unsafe tool-calling, silent action-taking)
• Build monitoring/auditing around AI tool use where it matters