Generative AI Applied Scientist - Services Special Projects - Jobs - Careers at Apple
- Train and fine-tune retrieval models: dense embedding models, late-interaction (multi-vector) models, and cross-encoder rerankers, with and without instruction conditioning.
- Fine-tune and adapt LLMs: including small, efficient LLMs for understanding, judgment, and evaluation tasks, using techniques such as LoRA/PEFT, distillation, and preference optimization (RLHF, DPO).
- Build training data: design training-data pipelines, including contrastive pairs, hard-negative mining, LLM-generated synthetic data, and data-quality analysis.
- Develop evaluation frameworks: measure retrieval and ranking quality (nDCG, recall, MRR) and model behavior, with automated and reproducible pipelines.
- Improve efficiency: analyze model internals and use quantization, pruning, and distillation to improve inference speed and cost without losing quality.
- Scale training and inference: run distributed training and high-throughput inference on GPU clusters, working with engineers on production serving.
- Collaborate cross-functionally: partner with ML scientists, engineers, and product teams from concept to launch.
- Communicate: present results to technical and leadership audiences, and contribute to the research community where appropriate.
- 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
- * 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
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant
At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Learn about accessibility in Apple’s workplace
Learn about reasonable accommodations for job applicants
Apple accepts applications to this posting on an ongoing basis.
Required Skills
Required Languages
🇬🇧 English