- Develop models, tools, metrics, and datasets for assessing and evaluating the safety of generative models over the model deployment lifecycle
- Develop methods, models, and tools to interpret and explain failures in language and diffusion models
- Build and maintain human annotation and red teaming pipelines to assess quality and risk of various Apple products
- Prototype, implement, and evaluate new ML models and algorithms for red teaming LLMs
Requirements
BS, MS or PhD in Computer Science, Machine Learning, or related fields or an equivalent qualification acquired through other avenues
Proven track record of contributing to diverse teams in a collaborative environment
Strong engineering skills and experience in writing production-quality code in Python, Swift or other programming languages
Background in generative models, natural language processing, LLMs, or diffusion models
Experience with failure analysis, quality engineering, or robustness analysis for AI/ML based features
Experience working with crowd-based annotations and human evaluations
Experience working on explainability and interpretation of AI/ML models
Work with highly-sensitive content with exposure to offensive and controversial content
Pay & Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Additional Information
Would you like to play a part in building the next generation of generative AI applications at Apple? We're looking for scientists and engineers to work on ambitious projects that will impact the future of Apple, our products, and the broader world.
In this role, you'll have the opportunity to tackle innovative problems in machine learning, particularly focused on generative AI. As a member of the Apple HCMI group, you will be working on Apple's generative models that will power a wide array of new features. Our team is currently working on large generative models for vision and language, with particular interest on safety, robustness, and uncertainty in models.
Apple · Seattle, WA