Machine Learning for GenAI, AiDP

Apple's Generative AI team is looking for a creative and driven ML Engineer to help shape the future of AI-powered experiences across the enterprise. In this role, you will work on cutting-edge systems that enable large-scale training, inference, and deployment of generative models. If you are passionate about building production-grade ML systems, solving hard research-backed engineering problems, and contributing to innovations that impact millions of users, we want to hear from you.

Description

You are a creative problem solver with strong ML and engineering skills who thrives in a fast-paced environment, working across teams and organizations. You enjoy learning new technologies and have a deep interest in ML/GenAI models and systems. You take ownership of your work, communicate scope clearly, and are motivated by the impact your models and systems will have on real-world users. Most importantly, you care about building responsibly and sharing your learnings with the broader ML community. The main responsibilities for this position include:

Responsibilities

• Develop ML/GenAI models and pipelines to solve for business KPIs at scale
• Innovate training pipelines using cutting-edge distributed systems and hardware-aware optimization
• Partner with cross-functional teams to translate groundbreaking research into user-centric products
• Tackle unique challenges in privacy-preserving generation, efficient inference, and multimodal integration
• Deliver production-grade models that meet Apple's rigorous standards for quality, performance, and scalability

• Bachelor of Science in Computer Science, Machine Learning, or a related quantitative field or equivalent experience
• 2+ years of hands-on experience in machine learning engineering
• 1+ years focused on generative AI or LLM technologies, or Agentic workflows
• Solid experience in Python
• Experienced building ML frameworks (PyTorch, JAX) for training, fine-tuning, and deploying generative models at scale
• Experience building enterprise-grade ML pipelines (data prep, distributed training, optimization, monitoring) in cloud environments (AWS, GCP, Azure) or on-prem infrastructure
• Deep understanding of transformer architectures, prompt engineering, retrieval-augmented generation (RAG), and LLM evaluation methodologies
• Experience optimizing models for latency, cost, and scalability (quantization, distillation, hardware-aware ML)

• Contributions to major open-source ML frameworks or research communities
• MS or PhD in Computer Science, Machine Learning, or a related quantitative field
• Solid grasp of NLP techniques, multimodal AI (text, image, code), and agent workflows.
• Experience with LLM Agentic workflows and framework (Langchain, LangGraph, LlamaIndex, CrewAI etc.)
• Knowledge in compiler/runtime optimizations for machine learning workloads