AIML - Machine Learning Researcher - MLR

Help shape the next generation of machine learning technology at Apple. The Machine Learning Research team in Barcelona pursues research and development in machine learning (ML), with a particular focus on introspection, robustness, and next-generation architectures.

As a member of the team, you will tackle some of the most challenging technical problems in the field, collaborate with world-class researchers and engineers, and engage with the academic community by publishing your work and speaking at top-tier conferences. Your research will influence future Apple products and reach millions of users worldwide.

If you are excited about advancing the state of the art and translating bold ideas into real-world impact, we'd love to meet you.

Are you passionate about advancing the state of the art and pursuing meaningful innovation? Are you interested in developing machine learning algorithms that power extraordinary products?

In this role, you will

Identify gaps in the research landscape, define a research agenda, and implement innovative ML approaches to address them. Design, run, and analyze experiments at scale, iterating from prototypes to robust implementations. Prepare technical reports and papers for publication, and present results at conferences and internal forums.

Provide technical mentorship and guidance to peers and partner teams. Collaborate with engineering and product teams to integrate research outcomes into Apple products, ensuring the highest standards of quality, scientific rigor, and respect for user privacy.

In-depth expertise in machine learning (ML) and deep learning (DL), with particular experience with transformers, or diffusion, or SSM architectures. Strong mathematical foundation in linear algebra, probability and statistics. Hands-on experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow.

Strong publication record in top ML venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP). Ability to formulate a research problem, design experiments, and implement solutions end-to-end. Excellent written and verbal communication skills, with the ability to present technical work to both expert and non-expert audiences.

Ability to focus and simplify when navigating ambiguous, fast-moving research problems, and to work both independently and collaboratively across teams and disciplines.

Interpretability and introspection of large neural networks. Training of large-scale models, including distributed training, optimization at scale, and efficiency techniques. Exploration of alternative architectures to attention-based models (e.g., state-space models, recurrent or linear-attention variants, or other emerging approaches).