What are the responsibilities and job description for the AIML - Senior ML Engineer, Responsible AI and Safety position at Apple, Inc.?
Join Us in Shaping the Future of Generative AI at Apple! Are you passionate about making AI systems safer, more inclusive, and globally representative? Apple is seeking an expert Machine Learning Engineer to shape the future of responsible AI for the next generation of generative features. In this role, you will lead the responsible AI lifecycle end-to-end: assessing risks, defining policies, developing mitigation strategies, and driving continuous improvements. Your work will directly influence how we evaluate, align, and monitor the safety of large language and multimodal models. As part of Apple's Responsible AI group within the Human-Centered Machine Intelligence (HCMI) organization, you'll collaborate with cross-functional partners to minimize unintended consequences across people, systems, and society while elevating feature capabilities and the overall user experience. Together, we'll anticipate challenges, measure real-world impact, and deliver trusted, high-quality AI experiences to users around the globe. You'll also contribute to forward-looking research in fairness, robustness, uncertainty, and safety - pushing the boundaries of responsible AI at scale.
Our team leads Responsible AI initiatives for global generative AI products, operating at the intersection of policy, product, and GenAI. We're seeking candidates who will shape safety policies in partnership with leadership, design, engineering, legal, and regulatory stakeholders-ensuring our safeguards advance both user protection and product innovation.\n\nThese individuals will work on architecture mitigation and safety alignment strategies for generative models, drive integration in production. Additionally, they will work on developing models, tools, datasets, and evaluation methods to monitor, diagnose failures, and improve the safety of generative models throughout the deployment lifecycle. We do all these by incorporating human and automated feedback, post-launch to continuously improve feature safety and user trust.
3 years of proven ability in machine learning, including work with generative models (Transformers, LLMs, VLMs), NLP, or Computer Vision\nProficiency in Python and data science libraries (e.g. Pandas) with strong skills in data analysis, visualization, and applied ML workflows\nExcellent interpersonal skills and proven ability to translate sophisticated technical insights for cross-functional partners, senior leadership, and executives\nStrong analytical and independent problem-solving skills, with ability to navigate ambiguity\nExperience designing and supporting human and automated evaluations, particularly with complex, nuanced, or multi-labeled data\nHands-on experience collecting and analyzing language, vision, or multimodal datasets\nBackground in failure analysis, quality engineering, or robustness testing for ML-driven systems\nMust be comfortable working with sensitive or potentially offensive content
BS, MS, or PhD in Computer Science, Machine Learning, or related field, or equivalent experience\nProven success contributing in a highly cross-functional environment\nExperience shipping complex AI systems at global scale\nBackground in model explainability, uncertainty estimation, or interpretability\nCuriosity and research interest in fairness, bias, and the societal impacts of generative AI\nPassion for building innovative, high-impact products that draw upon interdisciplinary skills
Our team leads Responsible AI initiatives for global generative AI products, operating at the intersection of policy, product, and GenAI. We're seeking candidates who will shape safety policies in partnership with leadership, design, engineering, legal, and regulatory stakeholders-ensuring our safeguards advance both user protection and product innovation.\n\nThese individuals will work on architecture mitigation and safety alignment strategies for generative models, drive integration in production. Additionally, they will work on developing models, tools, datasets, and evaluation methods to monitor, diagnose failures, and improve the safety of generative models throughout the deployment lifecycle. We do all these by incorporating human and automated feedback, post-launch to continuously improve feature safety and user trust.
3 years of proven ability in machine learning, including work with generative models (Transformers, LLMs, VLMs), NLP, or Computer Vision\nProficiency in Python and data science libraries (e.g. Pandas) with strong skills in data analysis, visualization, and applied ML workflows\nExcellent interpersonal skills and proven ability to translate sophisticated technical insights for cross-functional partners, senior leadership, and executives\nStrong analytical and independent problem-solving skills, with ability to navigate ambiguity\nExperience designing and supporting human and automated evaluations, particularly with complex, nuanced, or multi-labeled data\nHands-on experience collecting and analyzing language, vision, or multimodal datasets\nBackground in failure analysis, quality engineering, or robustness testing for ML-driven systems\nMust be comfortable working with sensitive or potentially offensive content
BS, MS, or PhD in Computer Science, Machine Learning, or related field, or equivalent experience\nProven success contributing in a highly cross-functional environment\nExperience shipping complex AI systems at global scale\nBackground in model explainability, uncertainty estimation, or interpretability\nCuriosity and research interest in fairness, bias, and the societal impacts of generative AI\nPassion for building innovative, high-impact products that draw upon interdisciplinary skills