DevSecOps Engineer

About The Role

We handle some of the most sensitive data around: patient outcomes, clinical trial records, and the multimodal datasets behind our foundation model, all inside a company that runs on agents with broad, standing access to internal systems. That combination raises the stakes on security in a way most companies never have to think about.

The threat landscape is moving fast too. Capable AI models have lowered the skill floor for finding and exploiting vulnerabilities, so the volume and sophistication of attacks aimed at companies like ours keeps climbing. We need someone who takes that seriously, builds for it before it's a problem, and doesn't treat security as something you check off at the end of a project.

We're hiring a DevSecOps engineer to own security architecture and practice across our Kubernetes-based AI and data infrastructure: the clusters, pipelines, agent tooling, and internal systems that power our BD, clinical development, computational biology, and lab teams. Kubernetes is central to how we run production, and deep, hands-on experience operating and securing it is essential. You'll design the guardrails, then get in the code and build them yourself.

What You'll Build

• Secure and operate our production Kubernetes infrastructure on GKE, including cluster configuration, workload isolation, RBAC, workload identity, network policies, secrets management, and container security.
• Build reliable deployment and operational practices for Kubernetes workloads, including observability, upgrades, troubleshooting, and incident recovery.
• Threat models and security architecture for a system built around large numbers of autonomous AI agents with access to internal tools, data, and MCP-style servers.
• Guardrails, sandboxing, and permissioning for how agents talk to systems and each other, so they can act without excessive standing privileges.
• Secure CI/CD pipelines, infrastructure-as-code review, and automated security testing (SAST/DAST, dependency and secret scanning) built into how we ship, not added on after.
• Monitoring, detection, and incident response tooling tuned for AI-native infrastructure, including anomalous agent behavior, prompt injection attempts, and data exfiltration paths.
• Data governance and access controls for a governed warehouse and knowledge graph holding patient-level and clinical trial data, aligned with HIPAA and relevant regulatory frameworks.
• Cloud security posture across our GCP environment: IAM, network segmentation, encryption, audit logging.

Who You Are

You've owned security for a production system end to end, from architecture through detection and response, somewhere a breach would be a real problem, not a hypothetical one. You think like an attacker and build like an engineer: you don't just write policy, you ship the code and tooling that make the secure path the easy path. You've kept up with how much easier AI has made attacks to pull off, and you turn that into concrete engineering decisions instead of vague concern. You move quickly, take initiative, and want to be judged on systems that hold up under real pressure, not paperwork that satisfies an audit.

Must-haves

• Roughly 6+ years in security engineering, DevSecOps, or something close, with real ownership of production security architecture.
• Deep, hands-on experience operating and securing Kubernetes in production. You've owned clusters and the workloads running on them, diagnosed failures across workloads, networking, storage, and access controls, and made concrete decisions about reliability and security. You can explain how the system works beneath the deployment tooling.
• Fluent in Python and/or TypeScript. You can read and write production code, not just configure tools.
• Deep experience with cloud security (GCP preferred, AWS or Azure fine too), IAM design, network security, and secrets management.
• Hands-on experience securing CI/CD pipelines and infrastructure-as-code (Terraform or similar).
• Practical experience building or operating detection and incident response systems, not just reading dashboards someone else set up.
• Comfortable working as a hands-on individual contributor alongside engineers, with ongoing ownership of Kubernetes infrastructure, deployment tooling, and platform reliability as well as security.

Bonus

• Experience securing agentic AI systems, LLM-powered applications, or MCP-style tooling, including prompt injection defense and agent permissioning.
• Experience in regulated environments handling patient or clinical trial data (HIPAA, GxP, or similar).
• Certifications like OSCP, CISSP, or GCP/AWS security certs are a nice signal but not a requirement.

Location

This is a hybrid role, requiring 3 days per week onsite, in our NYC Headquarters.