Principal Machine Learning Engineer at Apple

Location: London, England
Level: staff+
Posted: Mar 16, 2026

Description

Apple is hiring a Principal Machine Learning Engineer to lead the end-to-end lifecycle of large language models and generative internal AI systems at global scale. The role covers pre-training, reinforcement learning and human feedback, fine-tuning, alignment, evaluation, benchmarking, production inference, and the delivery of standalone AI-powered products. The engineer will work across Python, Swift, and Java, build enterprise-grade ML pipelines, set engineering standards, mentor engineers, and influence the technical roadmap for generative AI adoption.

Required Skills

LLM fine-tuning
RLHF
Prompt engineering
Python
Swift
Java
Distributed training
Inference optimisation

Additional Information

Required Previous Experiences: Extensive hands-on machine learning engineering experience and a track record of shipping machine learning-powered products at scale
Deep practical expertise in LLM fine-tuning, alignment, customisation, RLHF, parameter-efficient fine-tuning, prompt optimisation, and LLM evaluation and benchmarking
Strong software engineering proficiency across Python, Swift, and Java
Experience building and operating enterprise-grade machine learning pipelines in cloud or on-premises environments
Preferred Qualifications: Ability to deliver end-to-end AI products from experimentation through globally deployed production solutions
Published papers in top conferences in machine learning, statistics, mathematics, or computer science
Experience with large language model pre-training or continued pre-training, including data curation, curriculum design, and training stability at scale
Expertise in reinforcement learning techniques for model alignment and safety or red-teaming methodologies
Familiarity with advanced agentic frameworks and architectures, including multi-agent orchestration and tool use
Experience with multimodal AI systems and cross-modal reasoning
Track record of building and shipping standalone AI-native products with accountability for user impact and product quality
Contributions to open-source machine learning frameworks, published research, or relevant patents
Expertise in inference optimisation techniques, including quantisation, speculative decoding, KV-cache optimisation, and hardware-aware model compilation
Strong data engineering instincts for designing pipelines, curating training datasets, and producing high-quality aggregated datasets at scale
Technical leadership in setting architectural direction, driving cross-team alignment, and mentoring senior engineers

About Apple

Apple

technology industryCupertino, CA

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