Senior Machine Learning Engineer

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 Machine Learning Engineer 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. 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, 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.