Senior Machine Learning iOS Platform Engineer — Responsible AI and Safety
Apple · San Francisco Bay Area ·
- Seniority
- Senior
- Category
- ML ai
- Experience
- 5+ years
- Company size
- 1000+
Apple · San Francisco Bay Area ·
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 Client Engineer to own the integration of our Responsible AI mitigation assets across the full deployment surface, from on-device foundation models running on Apple Silicon to server-side inference on Private Cloud Compute (PCC).
In this role, you will be a pivotal technical leader, bridging Swift client engineering and ML deployment, driving the architectural vision, design, and implementation of how safety classifiers, guardrail models, and mitigation policies are shipped, invoked, and streamed alongside our generative features. You will take end-to-end ownership, from initial concept and rapid prototyping to delivering robust, high-performance, and maintainable solutions that 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.
Our team leads Responsible AI initiatives for global generative AI products, operating at the intersection of policy, product, and GenAI. We build the safety classifiers, content filters, and policy enforcement layers that protect users from unintended model behavior. This role is about getting those assets into users' hands reliably, on the device or in the cloud, at the latency and quality bar Apple expects. We are seeking candidates who will work closely with multiple stakeholders, ranging from design, engineering, legal and regulatory to ensure our safeguards advance both user protection and product innovation. You will work on defining mitigation architectures, owning the implementation and overseeing the integration in production. Additionally, you will contribute to modeling, tooling and frameworks, as well as dataset, and evaluation methods to monitor, diagnose failures, and improve the safety of generative models throughout the deployment lifecycle.