Senior Machine Learning iOS Platform Engineer — Responsible AI and Safety
Software Engineering, Data Science
San Francisco, CA, USA · New York, USA · San Francisco Bay Area, CA, USA · Multiple locations
USD 181,100-318,400 / year + Equity
Posted on Jun 10, 2026
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.
- Architectural Leadership: Defining and evolving the technical architecture for complex multimodal workflows, ensuring scalability, performance, and future extensibility.
- Feature Innovation: Leading the design and implementation of cutting-edge features that leverage Apple's unique hardware and software capabilities.
- Technical Mentorship & Growth: Guiding and mentoring a team of talented engineers, fostering best development practices, and contributing to the growth and culture of the team.
- Cross-Functional Collaboration: Partnering closely with other engineering teams to translate Safety needs into same and performant software experiences.
- Performance Optimization: Ensure our features set the benchmark for speed and efficiency, by expertly optimizing for large-scale image processing and real-time interactions, resulting in an unparalleled user experience.
- Contribute to deployment pipeline and tooling efforts as needed
- 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
- 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