About Suno

We're building the world's first creative entertainment platform, where the entire world can feel the joy and fulfillment of making music. Music is for everyone: Our users include everyone from grandmothers creating songs for their loved ones, to Grammy winners using Suno Studio, our power tool, to make the most popular hits in the world.

Building the future of entertainment requires ambition. The pace is fast, the problems are hard, and the work demands ownership and intensity. For the right people, it’s incredibly rewarding: a chance to shape a new medium, work with a small team that cares deeply about quality, make music, drink too much coffee, and build something that millions of people use to express themselves in ways that were never before possible.

Suno is the fastest growing consumer entertainment company and the leader in AI music. We are backed by leading investors including Bond Capital, Menlo Ventures, Lightspeed Venture Partners, IVP, Forerunner, Union Square Ventures, Alkeon, Quiet, Matrix Partners, Schroders Capital and, NVentures (venture arm of NVIDIA).

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.

Listen to the song we made about it: https://suno.com/s/JJoqlgtg8424Uq68

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