Senior Software Engineer, Backend & AI Platform - Sureel AI

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.

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. Make architecture decisions across databases, queues, caching, distributed systems, compute, storage, networking, reliability, and cost. Work directly with Product and, when needed, customers or AI partners to turn complex requirements into scalable technical solutions. Build internal tooling and automation that makes the entire engineering and research team more effective. AI-native engineering AI is part of how we expect the engineering team to work. You should actively use and experiment with coding agents, agentic frameworks, AI-assisted debugging and research, automated testing, and other emerging development workflows. The goal is to use these tools to move faster without lowering the quality bar. You should understand the code and systems you ship, recognize when an AI-generated solution is wrong, challenge weak architectural decisions, and solve difficult problems independently. We are looking for engineers who use AI to extend their judgment and capabilities, not replace them.

You are a senior software engineer with strong depth in backend and platform engineering and the ability to work across the stack when needed. You have personally designed, built, deployed, and operated meaningful production systems. You are highly proficient in a modern backend language and comfortable working extensively with TypeScript/Node.js. Python experience is a bonus. You understand the fundamentals behind databases, networking, concurrency, APIs, distributed systems, queues, caching, cloud infrastructure, and failure modes. You can go beneath frameworks and abstractions when necessary rather than treating them as black boxes. You have strong cloud, DevOps, and production infrastructure experience. You understand ML systems well enough to work effectively with model serving, GPU workloads, inference, data pipelines, embeddings, evaluation, and MLOps. You use AI as part of your engineering workflow and look for practical ways to automate and accelerate your work. You reason from first principles and enjoy working through difficult technical problems. You care about the product and business outcome, not just the technical implementation. You are comfortable in a fast-moving startup environment where you will have significant ownership and ambiguity.

xperience with GCP, Kubernetes, Docker, Terraform, AWS DynamoDB or another NoSQL database, Meilisearch, vector search, distributed processing, GPU infrastructure, enterprise SaaS, developer APIs or SDKs, TypeScript/React, agentic development frameworks, or large-scale audio and media systems. Most importantly, we are building a team with high standards and low ego. We value people who are kind, intellectually curious, generous with knowledge, willing to challenge ideas, willing to change their minds, and genuinely supportive of their teammates.