About this role
Join our Apple Services Special Projects Team and help build the next generation of GenAI solutions for millions of people, upholding the user privacy and security that Apple customers know and expect!
Our team shapes the future of Apple's technologies, experiences, and services by building state-of-the-art models, infrastructure, and tools. If you want a collaborative environment where your expertise is valued, and you want to work on hard generative AI and retrieval problems that haven't been solved, this may be a role for you. We are especially interested in scientists who care about what and how models learn, and who use that understanding to make models more accurate, efficient, and robust.
We are seeking a Machine Learning Scientist with a strong track record in applied AI research. You will train and improve the models behind large-scale search, retrieval, and ranking systems, and turn the latest research into production-ready models.
Minimum Qualifications
PhD in Computer Science, Machine Learning, or a related field, or MS with 5+ years of applied ML research experience
Strong deep learning and NLP fundamentals, with hands-on experience training and fine-tuning transformer models
Proficiency in PyTorch and strong Python programming skills
Experience with post-training, fine-tuning, or representation learning for large models
Experience designing rigorous experiments and evaluations, and analyzing model behavior
Demonstrated ability to turn research ideas into working systems
Preferred Qualifications
* Experience training embedding, retrieval, or reranking models (e.g., contrastive learning, late-interaction / ColBERT-style models, cross-encoders)
* Experience with parameter-efficient fine-tuning (LoRA, QLoRA, PEFT) and preference optimization (RLHF, DPO)
* Experience with distributed training (e.g., FSDP, DeepSpeed) and multi-GPU workloads
Experience with inference engines and serving frameworks (vLLM, SGLang, Triton)
Experience with model efficiency: quantization, pruning, distillation, or analysis of model internals
Experience with data-centric ML, such as data curation, data attribution, or training-data robustness
Familiarity with cloud environments (AWS or GCP) and containerization (Docker, Kubernetes)
Publications at top venues (e.g., NeurIPS, ICLR, ICML, CVPR, ACL), and service as a reviewer or program committee member