Senior Applied Researcher

** Weekly

Hours:

** 40

** Role Number:*
* ** Summary*
* 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.

** Description*
* 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.

** Minimum Qualifications*
* + 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 Tensor Flow, 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.

** Preferred Qualifications*
* + 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