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.