- Innovate training pipelines using cutting-edge distributed systems and hardware-aware optimization
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
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.