Ready to transform how billions of people interact with technology? Apple’s Core Foundation Models team is driving the intelligence that powers experiences across billions of devices worldwide. Join our Europe-based applied ML team building the next generation of large-scale ML and RL training infrastructure for Apple’s foundation models. We develop high-performance, distributed systems that power cutting-edge foundation model research on a massive scale.
If you thrive at the intersection of distributed systems, ML frameworks, and high-performance computing, this is the role for you.
Tasks
- Design, build, and scale systems that enable large-scale reinforcement learning for Apple’s foundation models.
- Focus on TPU-based training with JAX, developing robust, high-performance RL pipelines that support distributed actor/learner architectures, efficient experience replay, and large-scale environment execution.
- Work across the full stack of RL training systems, from low-level performance tuning and compiler optimization to cluster-level orchestration and resource management.
- Ensure that training pipelines are efficient, reliable, reproducible, and observable, enabling research teams to iterate quickly and explore more complex RL environments and models.
- Collaborate with researchers and systems engineers to build robust training frameworks, accelerate experimentation, and push the boundaries of performance and efficiency.
- Work with teams across Apple’s engineering hubs, including New York, Seattle, and Cupertino, to advance the tooling and systems that make large-scale model training possible.
Requirements
- PhD or MSc in Computer Science, Computer Engineering or a closely related field.
- Hands-on experience designing, building, or maintaining large-scale ML training infrastructure.
- Strong proficiency with PyTorch or JAX and experience running training workloads on GPUs/TPUs.
- Solid understanding of distributed systems concepts, including parallelism strategies, fault tolerance, and synchronization.
- Practical experience developing or optimizing training loops, RL pipelines, or large-scale model-training frameworks.
- Strong software engineering skills in Python, with emphasis on reliability, debuggability, and high-performance execution.
- Deep experience with PyTorch/JAX internals, XLA, debugging and performance profiling on GPU/TPU architectures.
- Expertise in distributed RL training patterns, including actor/learner architectures, experience replay, and parallel environment execution.
- Experience building training services, orchestration tools, or automated pipelines for large-scale experiments.
- Proven success diagnosing bottlenecks in large-scale ML jobs (I/O, input pipelines, kernel performance, memory, compilation).
- Familiarity with RL-specific infrastructure requirements (e.g., actor/learner architectures, experience replay systems, large-scale environment execution).
- Strong software engineering practices: code quality, design reviews, testing, observability, CI/CD.
- Experience working with cloud-scale clusters or specialized accelerators (TPU v5/v6, GPU, custom hardware).
- Contributions to ML frameworks, distributed training libraries, or high-performance computing systems.
- Excellent communication and collaboration skills for working with research and engineering partners.
Bist du Teil dieses Unternehmens?
Dieses Unternehmensprofil wurde automatisch erstellt. Wenn du für Apple Switzerland AG arbeitest, kannst du das Profil jetzt beanspruchen und verifizieren – kostenlos und in wenigen Minuten.
Über uns
Das Team
Join a Europe-based applied ML team collaborating with researchers, systems engineers, and teams across Apple’s engineering hubs in New York, Seattle, and Cupertino.