Principal Applied Researcher

Apple Apple · Big Tech · Cupertino, CA +1 · Machine Learning and AI

Seeking a Senior Applied Researcher with expertise in Generative AI and LLM architectures to influence and implement foundational intelligence capabilities across Apple Services. The role involves architecting, designing, and deploying LLM-powered systems, leading research in areas like representation learning and RAG, driving LLM fine-tuning and evaluation, and exploring advanced methods like parameter-efficient adaptation and multi-agent orchestration. The goal is to build production-grade solutions that advance Apple's reasoning capabilities over text and behavioral signals at scale.

What you'd actually do

  1. Architect, design, and deploy LLM-powered systems that unlock new capabilities for personalization, intelligent automation, and customer understanding across Apple’s services ecosystem.
  2. Lead research in areas such as large-scale representation learning, semantic modeling, topic induction, natural language understanding, and retrieval-augmented generation (RAG).
  3. Drive LLM fine-tuning, evaluation, safety alignment, and optimization strategies to ensure performant, compliant, and frictionless user experiences.
  4. Explore and productize methods such as parameter-efficient adaptation, multi-agent orchestration, active learning, RLHF, and novel inference optimization techniques.
  5. Collaborate with engineering, product, and design organizations to translate ambiguous problem spaces into robust ML systems with measurable business and customer impact.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • Spark
  • deep learning
  • modern NLP
  • transformer architectures
  • foundation model adaptation
  • LLM model development
  • fine-tuning
  • instruction tuning
  • prompt engineering
  • domain-specific reasoning
  • distributed data processing systems
  • large-scale experimentation
  • communication of research outcomes

Nice to have

  • Retrieval-augmented generation (RAG) pipelines
  • vector-based semantic search systems
  • Representation learning
  • semantic embeddings
  • clustering
  • categorization
  • content understanding
  • Model evaluation frameworks
  • language quality
  • relevance
  • hallucination
  • safety
  • Inference optimization techniques
  • quantization
  • distillation
  • model compression
  • reinforcement learning
  • policy alignment
  • RLHF
  • interactive AI systems
  • personalization
  • ranking
  • optimization algorithms
  • architectural design
  • RL experiment platform
  • multi-armed bandit experiment platform
  • publications in top-tier ML/AI venues
  • patent filings

What the JD emphasized

  • production deployment
  • production AI systems
  • production-grade solutions

Other signals

  • LLM architectures
  • Generative AI
  • production deployment
  • large-scale innovation