Principal Applied Researcher

Apple Services Engineering is seeking a Principal Applied Researcher for the Commerce & Growth Intelligence organization. This role involves architecting, designing, and deploying LLM-powered systems for personalization, intelligent automation, and customer understanding across Apple’s services. The researcher will lead innovation in Generative AI, LLM architectures, and advanced NLP, taking novel ideas from research to production. Key areas include large-scale representation learning, RAG, LLM fine-tuning, and optimization, collaborating across engineering, product, and design teams to deliver high-impact solutions.

Must Have

• Architect, design, and deploy LLM-powered systems
• Lead research in Generative AI, LLM architectures, and advanced NLP
• Drive LLM fine-tuning, evaluation, safety alignment, and optimization
• Ph.D. in Computer Science, Machine Learning, NLP, Statistics, or equivalent industry experience
• Expert knowledge of deep learning and modern NLP, including transformer architectures
• Experience with LLM model development, fine-tuning, instruction tuning, and prompt engineering
• Proficiency in Python and ML frameworks like PyTorch or TensorFlow
• Experience deploying models in production systems
• Strong understanding of distributed data processing systems and large-scale experimentation
• Ability to communicate research outcomes and technical tradeoffs

Good to Have

• Hands-on experience with Retrieval-augmented generation (RAG) pipelines
• Experience with vector-based semantic search systems
• Experience with Representation learning and semantic embeddings
• Experience with Model evaluation frameworks for language quality, relevance, hallucination, and safety
• Experience with Inference optimization techniques (quantization, distillation, model compression)
• Understanding of reinforcement learning, policy alignment, or RLHF
• Experience developing personalization, ranking, or optimization algorithms at scale
• Record of publications in top-tier ML/AI venues or patent filings

Perks & Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Range of discounted products and free services
• Reimbursement for certain educational expenses (tuition)
• Opportunity to become an Apple shareholder through discretionary employee stock programs
• Opportunity to purchase Apple stock at a discount through Employee Stock Purchase Plan
• Eligibility for discretionary restricted stock unit awards
• Eligibility for discretionary bonuses or commission payments
• Relocation assistance

Apple Services Engineering 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 Commerce & Growth Intelligence 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.

In this role, you will:

• 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
• A record of publications in top-tier ML/AI venues or patent filings demonstrating novel research contributions

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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.

Apple accepts applications to this posting on an ongoing basis.