AI Security Specialist

About the Role
We're looking for a Staff AI Security Engineer to secure how Suno's models are built, trained, and served, and to build the systems that keep them that way. You'll work across the full model lifecycle, from training pipelines and compute infrastructure to the products millions of people create with. You'll partner closely with ML research, platform, and product engineering, and set the technical direction for AI security as the function grows. There's a lot to build, and you'll be building it.

What You'll Do
Design and build security controls for model training and serving infrastructure, including compute clusters, pipelines, access to model artifacts, and the software supply chain behind them

Engineer detection, monitoring, and guardrail systems for model inputs, outputs, and agentic tool use, as production code rather than one-off scripts

Break our own systems through adversarial testing and red-teaming across models, pipelines, and products, then drive the fixes to done

Partner with ML research and platform teams to make secure defaults the easy path in how models are trained, evaluated, and deployed

Partner with product engineering to threat model and review customer-facing AI features before launch, and build the shared controls that let teams ship them safely by default

Set technical direction for AI security at Suno, including standards, reference architectures, and the roadmap, while raising the security bar across engineering

Help make creative fulfillment a daily reality for everyone by keeping the platform trustworthy enough that millions of people want to create on it

What You'll Need
8–10 years in security engineering, including hands-on work securing ML systems or large-scale ML infrastructure in production

A track record of designing and shipping security systems and tooling, not only assessing them

Deep knowledge of cloud and infrastructure security: IAM, Kubernetes, network segmentation, secrets management, and infrastructure as code

Strong understanding of ML training and serving pipelines and their attack surface, including model supply chain risks and protecting model weights and artifacts

Fluency with LLM and generative AI attack vectors and frameworks such as OWASP Top 10 for LLMs and MITRE ATLAS

Strong coding skills in Python or Go

Experience setting technical direction and influencing senior engineers and leaders without formal authority

Nice-to-Haves

Experience at a foundational model company or an AI research lab

Securing GPU compute environments or distributed training at scale

Hands-on use of adversarial testing tools such as Garak or PyRIT

Background securing audio, media, or other generative content pipelines