About the job
FriendliAI is looking for an engineer to architect and build the security foundations of a multi-tenant serverless compute platform running on Kubernetes with hardened container isolation. As a Software Engineer, Platform Security, you will own the security design for both the control plane and data plane, implement guardrails as code, and partner closely with Platform/SRE/Infra teams to ship a secure-by-default developer experience. This is a hands-on builder and architect role.
Key Responsibilities
Design and implement the security architecture for a Kubernetes-based, multi-tenant serverless platform.
Build guardrails as code using Terraform and Helm.
Establish network segmentation and service-mesh policy.
Develop secrets and key management patterns.
Implement runtime detections for containerized workloads.
Design and roll out IAM & workload identity management.
Threat model new features and changes to the overall platform.
Collaborate with engineering to debug production issues, lead post-incident hardening, and automate evidence for compliance controls.
Qualifications
5+ years securing cloud-native platforms with a focus on distributed systems and multi-tenancy.
Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
Strong hands-on experience with AWS (IAM, VPC/networking, logging/monitoring, …) and Kubernetes/container security.
Proficiency with Terraform and Helm.
Experience building or operating runtime detections for containers/functions.
Strong technical background in backend systems, cloud infrastructure, or AI tooling
Clear written and verbal communication; ability to translate pricing/business rules into robust systems.
Preferred Experience
Knative or similar serverless-on-K8s frameworks.
Container hardening and sandboxing.
Policy-as-code and admission policy design at scale.
eBPF-based observability, detections, and signal tuning.
Multi-cloud exposure and cross-cloud identity approaches.
Hands-on contributions to SOC 2 / HIPAA control automation and audit evidence pipelines.
Benefits
A front-row seat to the generative AI infrastructure revolution
Competitive compensation and benefits package
Daily lunch and dinner provided; unlimited snacks and beverages
Health check-up and top-tier hardware support
Flexible working hours and a highly collaborative environment
About us
FriendliAI is building the next-generation AI inference platform that accelerates the deployment of large language and multimodal models with unmatched performance and efficiency. Our infrastructure powers high-throughput, low-latency workloads for global organizations and integrates directly with Hugging Face, providing instant access to over 600,000 open-source models. We are on a mission to deliver the world’s best platform for AI inference.
Skills Required
- 5+ years securing cloud-native platforms, with a focus on distributed systems and multi-tenancy
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience
- Hands-on experience with AWS, including IAM, VPC/networking, and logging/monitoring
- Hands-on experience with Kubernetes and container security
- Proficiency with Terraform and Helm
- Experience building or operating runtime detections for containers or functions
- Strong technical background in backend systems, cloud infrastructure, or AI tooling
- Clear written and verbal communication skills
- Ability to translate pricing and business rules into robust systems
- Experience with Knative or similar serverless-on-Kubernetes frameworks
- Experience with container hardening and sandboxing
- Experience with policy-as-code and admission policy design at scale
- Experience with eBPF-based observability, detections, and signal tuning
- Multi-cloud exposure and cross-cloud identity experience
- Experience automating SOC 2 or HIPAA controls and audit evidence pipelines
What We Do
FriendliAI is The Frontier AI Inference Cloud: an AI infrastructure platform that deploys, scales, and monitors large language and multimodal models. Its inference engine maximizes GPU utilization to deliver faster performance and steep cost savings for open-weight and custom models, while offering enterprise-grade reliability, SLAs, and compliance to help teams run generative AI and agent workloads at production scale.
