Senior AI Engineer, 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.

Our work involves technical problems that often have no established solutions. We study how data influences generative models, trace influence through training and inference, and develop attribution and protection systems that turn advanced AI research into reliable enterprise products.

Your role

We are looking for a Senior AI Research Engineer who combines deep scientific understanding with strong engineering skills.

You will tackle problems where existing methods may not be enough. You'll develop hypotheses from first principles, explore new approaches, adapt existing research, design rigorous experiments, and turn successful ideas into production systems.

You'll need to stay current with AI research, recognize when established methods are useful, and know when to challenge or extend them. Some problems will call for applying state-of-the-art techniques. Others will require substantial changes or entirely new approaches.

You'll work across the full research and engineering cycle, from formulating a problem and testing an idea to deploying it, learning from real-world results, and improving the underlying science.

We benchmark our work against leading academic institutions and industrial AI labs. We want to develop original methods that stand up to that level of scrutiny and can also become useful products.

What you'll do

• Take on difficult AI problems from initial hypothesis and algorithm design through validation, deployment, and iteration.

• Develop new algorithms, representations, objectives, evaluation methods, and system designs when existing approaches fall short.

• Evaluate, challenge, and extend current research, testing published methods against our specific problems and goals.

• Design rigorous benchmarks, ablation studies, and statistical evaluations, comparing our methods with leading academic and industry approaches.

• Work across areas including training-data attribution, model influence, interpretability, embeddings, multimodal similarity, AI detection, guardrails, privacy-preserving observability, and large-scale retrieval.

• Build prototypes quickly and turn successful approaches into scalable production systems.

• Work closely with Engineering and Product to translate scientific advances and real-world problems into useful product capabilities.

• Improve our research and MLOps infrastructure to support rapid experimentation, reproducibility, deployment, monitoring, and iteration.

• Strengthen Sureel's technical capabilities through novel methods, proprietary datasets, evaluation frameworks, patents, and, where useful, research publications.

AI-native engineering

We expect you to actively use AI tools to improve how you research, experiment, and build.

That includes coding agents, agentic frameworks, AI-assisted debugging and research, automated testing, and emerging development workflows. We want you to experiment with these tools and find ways to make your work faster and more effective without compromising rigor or quality.

You remain responsible for the work you ship. That means understanding the code and systems you build, checking generated solutions critically, challenging weak technical decisions, and solving difficult problems independently. AI should extend your capabilities and judgment, not replace them.

Who you are

• You have a strong scientific foundation in machine learning, AI, computer science, electrical engineering, mathematics, physics, or another quantitative field.

• You have an advanced degree or equivalent scientific depth developed through substantial research and engineering experience.

• You understand machine learning deeply enough to change an algorithm, architecture, objective, representation, training procedure, or evaluation method when the problem calls for it.

• You can reason from first principles, develop new hypotheses, and use experiments to determine whether your ideas work.

• You've demonstrated technical originality through research, algorithms, patents, open-source contributions, unconventional engineering solutions, or products built without an established playbook.

• You are highly proficient in Python and modern ML frameworks such as PyTorch.

• You understand modern…