As a pivotal member of Apples enterprise generative AI efforts you will:- 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 Apples rigorous standards for quality performance and scalability
Bachelor of Science in Computer Science Machine Learning or a related quantitative field or equivalent experience
5 years of hands-on experience in machine learning engineering with 2 years focused on generative AI and LLM technologies and Agentic workflows
Expertise in Python and ML frameworks (PyTorch JAX) for training fine-tuning and deploying generative models at scale
Proven track record of 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
Solid grasp of NLP techniques multimodal AI (text image code) and agent workflows.
Experience optimizing models for latency cost and scalability (quantization distillation hardware-aware ML)
MS or PhD in Computer Science Machine Learning or a related quantitative field
Experience with LLM Agentic workflows and framework (Langchain LangGraph LlamaIndex CrewAI etc.)
Background in compiler/runtime optimizations for machine learning workloads
Contributions to major open-source ML frameworks or research communities
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