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Senior Applied Researcher
AppleSan Francisco, California, United StatesSenior$181,100 - $318,400
Apply Apple Services Engineering (ASE) builds experiences that touch hundreds of millions of customers every day. From personalized recommendations to proactive intelligence and large-scale commerce systems, we are transforming how users discover, engage, and transact across the Apple ecosystem. The ASE AI/ML organization sits at the nexus of these systems—where deep applied research, advanced machine learning, and large language models converge to drive high-impact innovation at unprecedented scale.
We are seeking a Senior Applied Researcher with demonstrated expertise in Generative AI, LLM architectures, and advanced NLP systems. This role is ideal for a hands-on technical leader who has taken novel ideas from research inception to production deployment, and who thrives in environments where ambiguity, scale, and cutting-edge innovation intersect.
Here, you won’t just push the boundaries of what’s possible today—you’ll define what’s next.
What Sets This Role Apart
This is not a narrow modeling position. You will influence and implement foundational intelligence capabilities across Apple Services—spanning language understanding, behavioral inference, discovery, and growth optimization—while operating with the autonomy and scope expected of senior technical leadership at Apple.
- Architect, design, and deploy LLM-powered systems that unlock new capabilities for personalization, intelligent automation, and customer understanding across Apple’s services ecosystem.
- Lead research in areas such as large-scale representation learning, semantic modeling, topic induction, natural language understanding, and retrieval-augmented generation (RAG).
- Develop and evolve taxonomies, embeddings, and model architectures capable of disentangling complex user or content behaviors in high-dimensional, unstructured data.
- Drive LLM fine-tuning, evaluation, safety alignment, and optimization strategies to ensure performant, compliant, and frictionless user experiences.
- Explore and productize methods such as parameter-efficient adaptation, multi-agent orchestration, active learning, RLHF, and novel inference optimization techniques.
- Collaborate 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 advance Apple’s ability to reason over text, behavioral signals, and domain-specific knowledge at scale.
- Contribute to Apple’s leadership in AI through patent filings, publications, and internal thought leadership.
- Mentor and elevate other researchers, fostering excellence in experimentation, code quality, communication, and scientific rigor.
- Ph.D. in Computer Science, Machine Learning, NLP, Statistics, or a related field—or equivalent industry experience delivering production AI systems.
- 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, with experience deploying models in production systems.
- Strong understanding of distributed data processing systems (e.g., Spark) and large-scale experimentation.
- Proven ability to communicate research outcomes, architectural decisions, and technical tradeoffs to technical and non-technical stakeholders.
- Hands-on experience with:
- Retrieval-augmented generation (RAG) pipelines and vector-based semantic search systems
- Representation learning and semantic embeddings for clustering, categorization, and content understanding
- Model evaluation frameworks for language quality, relevance, hallucination, and safety
- Inference optimization techniques (quantization, distillation, model compression)
- Understanding of reinforcement learning, policy alignment, or RLHF for improving interactive AI systems
- Experience developing personalization, ranking, or optimization algorithms at scale
- Proven experience in the architectural design and develop RL or multi-armed bandit experiment platform
- A record of publications in top-tier ML/AI venues or patent filings demonstrating novel research contributions