AIML - Annotation Engineer, Red Teaming & Safety Evaluation

Summary
Would you like to play a part in building the next generation of generative AI applications at Apple? We're looking for ML Researchers, ML Data Scientists, and ML 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, and uncertainty in models.



Description
Apple Intelligence is powered by high quality, detailed annotations in the specialized domains of linguistics and computer vision. This role is instrumental in coordinating and documenting data authoring, annotation, and grading projects with human authors/annotators ("red teamers"), data scientists, and machine learning engineers. Responsibilities include:

- Developing red teaming and grading project strategies, interfaces, and guidelines in consultation with data scientists and product engineering teams
- Relaying and interpreting project guidelines and instructions for red teamers
- Prioritizing and scheduling projects to align with organizational goals and timelines
- Serving as an interface for feedback and questions from red teamers back to data scientists, project managers, and product development engineers
- Monitoring and managing aggregate and individual red teamer performance metrics (accuracy, throughput, etc.) while providing feedback and guidance for improvement
- Working with data scientists and red teaming engineers to distill findings into recommendations for product engineering teams and safety policy development
- Researching, developing, and applying conventional and ML-based enhancements to red teaming and grading methodologies to improve efficiency and quality

---