Summary
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something – you’ll add something.
As part of Apple Intelligence, this team is at the forefront of the next revolution in machine learning and Generative AI. We are dedicated to creating groundbreaking conversational assistant technologies for both large-scale systems and new client devices, building upon our legacy of intelligent assistant solutions that already assist millions of users worldwide. Does the opportunity to play a part in building groundbreaking technology for large-scale systems, natural language and artificial intelligence excite you? Do you want to expand the experience of Siri and other AIML products to new products that will help millions get things done, across the globe? Join the ML Systems Evaluation Engineering (MLSEE) team at Apple and contribute to a highly accomplished team that evaluates AIML products, that will delight and inspire millions of people!
Description
As a Performance Framework Software Engineer, you will develop the solutions required to deliver successful AIML products. These include design of automation and performance frameworks and methodologies for evaluating scalable performance measurements for ML based products. This role requires building and implementing tools, APIs and infrastructure for ML based product qualification, and tracking user impact metrics. Equally, this role will participate in the AIML iOS products roadmap decisions, contributing insights and recommendations derived from the in-depth understanding of the current success metrics of AIML products.
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