Senior Security Engineer - Runware

About Runware

Runware is the AI inference platform that builds and runs its own infrastructure, end to end. Our Sonic Inference Engine has served more than 20 billion requests for over 1 million developers and 500 million end users, with 400,000+ models behind a single integration, at up to 10x lower cost per token than traditional providers.

We are now building the AI Factory that runs it. The Sonic Inference Pod packs 1 MW of custom-designed compute into a 20-foot container with closed-loop, water-free cooling, sited wherever power exists. Pods are live in the US and Europe, and we are scaling to dozens of pods and tens of thousands of GPUs, all operated as one distributed fabric that reroutes traffic the moment a pod goes offline.

Every one of those pods holds customer models, customer data and millions of dollars of GPUs, sometimes inside a customer's own perimeter or a sovereign jurisdiction. Keeping all of it secure without slowing anyone down is the job.

The Role

As Senior Security Engineer, you will own the security of Runware's AI supercomputer: the bare-metal GPU fleet, the Sonic Inference Pods it lives in, the managed Kubernetes clusters we hand to customers, and the control plane that ties it all together across the US and Europe. You will design the controls, build the first version with your own hands, and set the standard for how security is run across every pod.

This is a hands-on senior individual-contributor role. You will threat-model a new pod before it ships, harden the image that every GPU node boots from, prove to an enterprise customer's security team that their model weights never leave their tenant, and when something goes wrong, lead the response and fix the root cause so it never happens again. You will work directly with our CISO, with the inference engine, platform and hardware teams, and with the site teams deploying pods.

What You'll Do

• Fleet Security Architecture: Own host hardening for bare-metal GPU nodes, immutable images and a signed build pipeline, boot integrity, and the identity, secrets and access model for humans and services across every pod.
• Multi-Tenant Security: Secure multi-tenancy on shared infrastructure. Design and verify isolation between customer workloads at the network, Kubernetes, host and GPU layers.
• Kubernetes Security: Secure the managed Kubernetes offering through cluster hardening, admission control, RBAC, supply-chain controls, and clean customer handovers.
• Visibility & Detection: Build security visibility, logging, detection, alerting and audit trails from firmware to API.
• Threat Modeling: Lead security reviews and threat models for new pods, sites, servers and platform features.
• Incident Response: Run incident response for infrastructure, including investigation, containment, recovery and post-incident learning.
• Vulnerability Management: Own vulnerability management for firmware, kernel, drivers, GPU stack and container images across thousands of nodes.
• Enterprise Compliance: Answer customer security due diligence and partner with the CISO on SOC 2 and ISO 27001 controls.
• Sovereign & On-Premise Deployments: Design for data residency, in-country key management, and physical/remote-access controls.
• AI Acceleration: Use AI to automate evidence collection, log triage, configuration audits and runbooks.

What We're Looking For

• 8+ years in security or infrastructure engineering, securing production infrastructure at scale.
• A track record of building security controls and taking them from design to running across a fleet.
• Deep, current, hands-on Linux security expertise (kernel hardening, namespaces, cgroups, seccomp, eBPF, secure boot, TPM-based attestation).
• Strong Kubernetes security experience (RBAC, admission control, network policy, runtime security, image signing).
• Fluent in network security on real hardware (segmentation, VLAN/VXLAN, firewalls, out-of-band management).
• Experience with identity, secrets and PKI at scale.
• Fluent in Python or Go for tooling and automation.
• Daily user of AI to accelerate output for scripting, log analysis, code review and incident triage.
• Pragmatic risk assessor and clear communicator across engineering, executive, and customer teams.
• Ability to travel to sites in the US and Europe as needed.

Nice to Have

• Experience securing GPU clusters for AI training or inference (NVIDIA driver/fabric manager, MIG, confidential computing).
• Firmware and hardware security (UEFI, BMC, NIC firmware, Redfish, hardware root of trust).
• InfiniBand or RoCE fabrics and RDMA security models.
• Delivering SOC 2 or ISO 27001 in production.
• Offensive security experience (red teaming, penetration testing, vulnerability research).
• Experience with sovereign, air-gapped or regulated deployments.
• Physical security of data centers or edge sites.

What We Offer

• A rare mandate to secure an AI supercomputer from the firmware up.
• Meaningful stock options.
• Remote-first culture with twice-yearly in-person company retreats.
• Generous paid time off, including vacation, sick days and public holidays.
• Paid maternity, paternity and caregiver leave.