AIML – Senior Machine Learning Engineer, Data and Evaluation for Full-time

Summary
Would you like to play a part in building the next generation of generative AI applications at Apple? We’re looking for Machine Learning Engineers to work on ambitious projects that will impact the future of Apple, our products, and the broader world. This role is directed at assessing, quantifying, and improving the safety and inclusivity of Apple’s Generative-AI powered features and products.

In this role you’ll have the opportunity to tackle innovative problems in machine learning, particularly focused on large language models for text generation, diffusion models for image generation, and mixed model systems for multimodal applications. As a member of the Apple HCMI/Responsible AI group, you will be working on Apple’s generative models that will power a wide array of new features, as well as longer term research in the generative AI space. Our team is currently interested in large generative models for vision and language, with particular interest on safety, robustness, explainability, and uncertainty in models.

Description
Apple Intelligence is powered by thoughtful data sampling, creation, and curation; high quality, detailed annotations; and application of these data to evaluate and mitigate safety concerns of new generative AI features. This role heavily draws on applied data science, scientific investigation and interpretation, cross-functional communication and collaboration, and metrics reporting and presentation to stakeholders & decision-makers. Responsibilities include:

– Develop ML-based enhancements to red teaming, model evaluation, and other processes to improve the quality of Apple Intelligence’s user-facing products

– Build and prototype analysis pipelines iteratively to provide insights at a large-scale

– Identify data needs and work on creative solutions, scaling and expanding data coverage through human and synthetic generation methods

– Coordinate project development, safety evaluation, and mitigation implementation timelines and execution with cross-functional collaborators

– Distill project findings into recommendations for product engineering teams and safety policy development

– Work with highly-sensitive content with exposure to offensive and controversial content