AIML - Senior ML/RL Training Infrastructure Engineer, AFM
Apple · Zurich ·
- Seniority
- Senior
- Category
- Devops
Apple · Zurich ·
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—and we’re looking for exceptional talent to join us! 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. We are seeking an engineer who is passionate about designing, optimizing, and scaling the infrastructure that enables state-of-the-art machine learning and reinforcement learning workloads.
As a senior member of the team, you will work closely with researchers and systems engineers to build robust training frameworks, accelerate experimentation, and push the boundaries of performance and efficiency. You will collaborate 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. If you thrive at the intersection of distributed systems, ML frameworks, and high-performance computing, this is the role for you.
As a core member of our ML infrastructure team, you will design, build, and scale the systems that enable large-scale reinforcement learning for Apple’s foundation models. You will 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.
In this role, you will work across the full stack of RL training systems—from low-level performance tuning and compiler optimization to cluster-level orchestration and resource management. You will ensure that training pipelines are efficient, reliable, reproducible, and observable, enabling research teams to iterate quickly and explore more complex RL environments and models.
Your work will directly impact the scalability, throughput, and stability of RL experiments, helping to unlock new capabilities in agentic reasoning, decision-making, and policy learning for Apple’s foundation models. This position is ideal for engineers who enjoy distributed systems, high-performance ML frameworks, and building the infrastructure that makes large-scale RL research possible.