Senior Machine Learning Engineer

Apple Services Engineering is hiring a Senior Machine Learning Engineer to help build and lead LLM-powered systems that improve personalization, intelligent automation, and customer understanding across Apple’s services ecosystem. This role spans research to production, requiring deep expertise in Generative AI and advanced NLP systems.

What you’ll do

• Architect, design, and deploy LLM-powered systems for personalization, intelligent automation, and customer understanding.
• Lead research across large-scale representation learning, semantic modeling, topic induction, natural language understanding, and retrieval-augmented generation (RAG).
• Develop and evolve taxonomies, embeddings, and model architectures to disentangle complex user or content behaviors in high-dimensional, unstructured data.
• Drive LLM fine-tuning, evaluation, safety alignment, and optimization to support performant, compliant, and frictionless user experiences.
• Explore and productize approaches including parameter-efficient adaptation, multi-agent orchestration, active learning, RLHF, and novel inference optimization techniques.
• Partner with engineering, product, and design organizations to translate ambiguous problem spaces into robust ML systems with measurable business and customer impact.
• Build prototypes and production-grade solutions that help Apple reason over text, behavioral signals, and domain-specific knowledge at scale.
• Support Apple’s AI leadership through patent filings, publications, and internal thought leadership.
• Mentor and elevate other researchers to improve experimentation, code quality, communication, and scientific rigor.

What you bring

• Hands-on experience with RAG pipelines and vector-based semantic search systems.
• Representation learning and semantic embeddings for clustering, categorization, and content understanding.
• Model evaluation frameworks covering language quality, relevance, hallucination, and safety.
• Inference optimization experience such as quantization, distillation, and model compression.
• Knowledge of reinforcement learning, policy alignment, or RLHF for improving interactive AI systems.
• Experience building personalization, ranking, or optimization algorithms at scale.
• Proven experience designing and developing an RL or multi-armed bandit experiment platform.
• Record of publications in top-tier ML/AI venues or patent filings demonstrating novel research contributions.
• Ph.D. in Computer Science, Machine Learning, NLP, Statistics, or a related field (or equivalent industry experience) with delivery of production AI systems.
• At least 6 years of experience in an applied research or machine learning role.
• Expert knowledge of deep learning and modern NLP, including transformer architectures and foundation model adaptation.
• Experience with LLM model development including fine-tuning, instruction tuning, and prompt engineering for domain-specific reasoning.
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow, including production deployment.
• Strong understanding of distributed data processing systems (e.g., Spark) and large-scale experimentation.
• Ability to communicate research outcomes, architectural decisions, and technical tradeoffs to technical and non-technical stakeholders.

Tools and technologies

• LLM-powered systems, Generative AI, LLM architectures, advanced NLP systems
• Retrieval-augmented generation (RAG), vector-based semantic search systems, embeddings
• Python, PyTorch, TensorFlow, Spark
• Transformer architectures, foundation model adaptation, fine-tuning, instruction tuning, prompt engineering
• Parameter-efficient adaptation, multi-agent orchestration, active learning, RLHF
• Quantization, distillation, model compression
• Reinforcement learning, policy alignment, multi-armed bandit

Compensation and benefits

• Location: Cupertino, CA (onsite)
• Base pay range: USD 184,700 - 324,800 per yearly
• Comprehensive medical and dental coverage
• Retirement benefits
• A range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Discretionary employee stock programs (eligibility requirements apply)
• Discretionary restricted stock unit awards
• Employee Stock Purchase Plan (purchase Apple stock at a discount, if voluntarily participating)
• Discretionary bonuses or commission payments as well as relocation (as applicable)

Apple employees may have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs, and may purchase Apple stock at a discount through the Employee Stock Purchase Plan if voluntarily participating.