Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build 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.
Our Developer Publications Intelligence team seeks a creative engineer who has a robust knowledge of large language models (LLMs), Machine Learning infrastructure, and experience with LLM-evaluations at scale. Strong engineering fundamentals are required.
Description
As an Applied Machine Learning Engineer in the Developer Publications Intelligence team, you will join a multi-discipline team of passionate Machine Learning and Software engineers to build and integrate ML models into existing and future tools produced by Apple for third-party developers. We strive for excellence and believe strongly in the quality of our output. As a member of the team, you will work alongside a team of domain experts in specific core subject areas, enable cross functional collaboration with other departments at Apple, contribute to architecture discussions, code review and proposals.
","responsibilities":"Driving MLOps/LLMOps excellence within the team: including evaluation pipelines, monitoring, observability, and deployment best practices
Building and maintaining LLM evaluation pipelines to assess model quality, track regressions, and support continuous improvement cycles
Engaging in code review, pair programming and architecture discussions with other members of the team
Preferred Qualifications
BS, MS or PhD in Computer Science, Artificial Intelligence, or Machine Learning (or equivalent experience)
Experience with: Xcode, Swift and developing for Apple’s platforms
Familiarity with on-device LLMs
Minimum Qualifications
Strong programming skills (Python, Swift, Go or other language)
Experience with MLOps/LLMOps toolkits and frameworks
Comprehensive knowledge and hands-on experience with LLM evaluations
A learning attitude to continuously improve self and team
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