Senior Machine Learning iOS Platform Engineer — Responsible AI and Safety

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. Minimum Qualifications 12+ years of professional experience, with at least 5+ years in iOS / macOS application development in both Objective C and Swift Expertise in Apple's Core iOS and Foundation frameworks BS in Computer Science, Mathematics, Statistics, or a related field, or equivalent industry experience Experience in shipping impactful mobile frameworks used by others outside your direct team Experience leading the architecture and development of complex, high-performance production systems Demonstrated ability to technically lead projects, mentor engineers, and drive cross-functional initiatives from concept to delivery Excellent analytical, problem solving and communication skills Preferred Qualifications Working knowledge of on-device ML runtimes (Core ML, MLX, or equivalent) and the model-export lifecycle: converting trained models into shippable assets, and loading them efficiently at runtime Working knowledge of frontier/LLM models including token-streaming inference, tokenization, and buffering strategies Experience building applications that utilize modern ML/AI technology