Software Engineer

A little bit about our team

Sureel AI is building the standards and infrastructure for AI rights, licensing, attribution, protection, and intelligence, starting in music and expanding across media.

We are building the infrastructure that enables creators and rightsholders to control how their work is used by AI, protect their rights, understand its use and value, and participate in new AI licensing models. We also provide AI companies and digital platforms with the systems they need to access rights, permissions, attribution, guardrails, reporting, and licensing infrastructure programmatically.

Acquired by Warner Music Group, Sureel operates as an independent, federated company, combining startup speed with the reach of a global music company.

Your role

We are looking for a senior software engineer with deep experience in backend, platform, DevOps, and MLOps engineering.

You will help build the technical backbone of the Sureel platform, working closely with AI Research, Product, and the rest of Engineering to turn advanced technology into reliable enterprise products.

This is a highly hands-on role. You will design architecture and write code, owning systems from implementation through deployment and production. You should be comfortable moving between APIs, distributed systems, databases, cloud infrastructure, ML workloads, enterprise integrations, and, when needed, the frontend.

We use frameworks, managed services, and AI tools to move quickly, but we expect you to understand what is happening underneath them. You should be able to reason about why something works, where it could fail, and what to do when an abstraction breaks down.

What you'll do:

• Design and build scalable backend services, APIs, data pipelines, and platform infrastructure across the Sureel stack.

• Own systems end-to-end, from architecture and implementation through deployment, observability, debugging, and iteration.

• Build infrastructure for high-volume AI, media, attribution, search, retrieval, and enterprise workloads.

• Work with our AI team to move experimental models and algorithms into reliable production services.

• Build and improve our MLOps capabilities across model deployment, inference, versioning, evaluation, monitoring, and reproducibility.

• Own and improve DevOps and cloud infrastructure, including CI/CD, infrastructure-as-code, containers, orchestration, security, observability, and deployment reliability.

• Build enterprise capabilities such as authentication, RBAC, multi-tenancy, auditability, bulk operations, integrations, and configurable workflows.