OpenNash Jobs
Apple · Zurich, Switzerland

AIML - Senior ML/RL Training Infrastructure Engineer, AFM

Zurich, SwitzerlandSenior individual contributorIT & Infrastructure

Listed on Apple’s official careers site. Posted December 3, 2025. Check that it is still open and apply there.

Apply on Apple’s careers site →

Job description

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.

More jobs at Apple

Similar jobs at other companies

AIML Engineer

Tata Consultancy Services · Bengaluru

Lead Software Engineer - Python, Data, Cloud, AIML

JPMorganChase · LONDON, United Kingdom

Python/AIML Developer - Software Engineer III

JPMorganChase · GLASGOW, LANARKSHIRE, United Kingdom

AIML, Gen AI, RAG

Tata Consultancy Services · Hyderabad

OpenNash lists jobs from employers’ own careers sites and does not handle applications. Dates come from the employer where stated; “first seen” and “last checked” are our own observations.