Principal Machine Learning Engineer, AI & Data Platforms (AiDP)

Principal Machine Learning Engineer, AI & Data Platforms (AiDP)London, England, United Kingdom Corporate Functions

At Apple, we build AI systems that define experiences for billions of people and we do it with an unwavering commitment to privacy, performance, and craft. The AI & Data Platforms (AiDP) team is seeking a Principlal Machine Learning Engineer to lead the design, fine‑tuning, evaluation, and productionisation of large language models and generative internal AI systems at global scale. This is a deeply hands‑on, high‑impact role: you will work across the full model lifecycle, from reinforcement learning and upstream training through to deployment of standalone, customer‑facing products. The ideal candidate is equal parts researcher, engineer, and product builder. You bring authoritative depth in LLM customisation and alignment, a sharp instinct for performance and quality, and the ability to ship end‑to‑end AI‑powered products that meet Apple’s standard of excellence. If you thrive at the intersection of frontier model development, systems engineering, and product creation we want to hear from you.

DescriptionOur Principal Machine Learning Engineers are technical leaders who shape the direction of intelligent systems across Apple. In this role, you will own the end‑to‑end lifecycle of an internal generative AI system at global scale – from pre‑training LLM strategies and reinforcement learning from human feedback (RLHF) through fine‑tuning, alignment, evaluation, and production deployment. You will architect and deliver standalone AI‑powered products and platform capabilities that operate reliably at global scale. You will establish rigorous benchmarking and evaluation frameworks to measure LLM performance across accuracy, latency, safety, and fairness dimensions. You will drive model customisation strategies, including prompt engineering, parameter‑efficient fine‑tuning (LoRA, QLoRA), and full fine‑tuning, tailored to diverse product requirements. You will design and build production‑grade inference systems, working across Swift, Java, and Python to integrate ML capabilities seamlessly into Apple’s ecosystem. As a senior technical contributor, you will set engineering standards, mentor engineers, and influence the technical roadmap for generative AI adoption across the organisation.

ResponsibilitiesLead the end‑to‑end development and productionisation of LLM‑based systems, from upstream training and reinforcement learning (RLHF/RLAIF) through fine‑tuning, alignment, and deployment of standalone, globally scaled productsDesign and implement comprehensive LLM evaluation and benchmarking frameworks, assessing model quality, safety, bias, latency, and cost‑efficiency to inform model selection and customisation decisionsArchitect production inference infrastructure that meets Apple’s performance, privacy, and reliability standards at global scale, including model optimisation, quantisation, and efficient serving strategiesDrive model customisation and adaptation strategies (prompt engineering, retrieval‑augmented generation, parameter‑efficient and full fine‑tuning) to deliver differentiated product experiencesBuild end‑to‑end AI‑powered products and features, taking full ownership from problem definition and prototyping through production release, working across Swift, Java, and Python codebasesEstablish engineering excellence across the ML development lifecycle, including robust testing, reproducibility, monitoring, documentation, and CI/CD for model and data pipelinesPartner with research, product, design, and platform teams to translate emerging capabilities into scalable, user‑centric solutions — acting as a technical bridge between research innovation and product deliveryMentor and elevate ML engineers across the team, raising the bar on technical quality and fostering a culture of rigorous experimentation and engineering craftMinimum QualificationsExtensive hands‑on Machine Learning engineering experience, with a demonstrable track record of shipping ML‑powered products at scaleDeep, practical expertise in LLM fine‑tuning, alignment, and customisation – including reinforcement learning from human feedback (RLHF), parameter‑efficient fine‑tuning (LoRA, QLoRA), prompt optimisation and LLM evaluation and benchmarking strategies (accuracy, latency, safety, cost)Strong software engineering proficiency across Python, Swift, and Java, with the ability to contribute production‑quality code across Apple’s technology stackExperience building and operating enterprise‑grade ML pipelines (data preparation, distributed training, model optimisation, serving, and monitoring) in cloud (AWS, GCP, Azure) or on‑prem environmentsPreferred QualificationsDemonstrated ability to deliver end‑to‑end AI products – from problem framing and experimentation through to globally deployed, production‑grade solutionsPublished papers in top conferences in ML/Statistics/Maths/compsci.Experience with pre‑training or continued p