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

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 u0026 Data Platforms (AiDP) team is seeking a Principal 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 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 Apples standard of you thrive at the intersection of frontier model development systems engineering and product creation we want to hear from you.n

Our Principal Machine Learning Engineers are technical leaders who shape the direction of intelligent systems across 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 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 Apples 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.n

Lead 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 productsnDesign and implement comprehensive LLM evaluation and benchmarking frameworks assessing model quality safety bias latency and cost-efficiency to inform model selection and customisation decisionsnArchitect production inference infrastructure that meets Apples performance privacy and reliability standards at global scale including model optimisation quantisation and efficient serving strategiesnDrive model customisation and adaptation strategies (prompt engineering retrieval-augmented generation parameter-efficient and full fine-tuning) to deliver differentiated product experiencesnBuild 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 codebasesnEstablish engineering excellence across the ML development lifecycle including robust testing reproducibility monitoring documentation and CI/CD for model and data pipelinesnPartner 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 deliverynMentor and elevate ML engineers across the team raising the bar on technical quality and fostering a culture of rigorous experimentation and engineering craft

Extensive hands-on Machine Learning engineering experience with a demonstrable track record of shipping ML-powered products at scalenDeep 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)nStrong software engineering proficiency across Python Swift and Java with the ability to contribute production-quality code across Apples technology stacknExperience building and operating enterprise-grade ML pipelines (data preparation distributed training model optimisation serving and monitoring) in cloud (AWS GCP Azure) or on-prem environments

Demonstrated ability to deliver end-to-end AI products - from problem framing and experimentation through to globally deployed production-grade solutionsnPublished papers in top conferences in ML/Statistics/Maths/compsci. nExperience with pre-training or continued pre-training of large language models including data curation curriculum design and training stability at scalenExpertise in reinforcement learning techniques for model alignment (RLHF RLAIF DPO PPO) and safety/red-teaming methodologiesnDeep familiarity with advanced agentic frameworks and architectures (LangChain LangGraph DSPy AutoGen or equivalent) including multi-agent orchestration and tool usenExperience with multimodal AI systems (text image code speech) and cross-modal reasoningnTrack record of building and shipping standalone AI-native products - not just features - with direct accountability for user impact and product qualitynContributions to open-source ML frameworks published research or patents in relevant areasnExpertise in inference optimisation techniques: quantisation (GPTQ AWQ) speculative decoding KV-cache optimisation and hardware-aware model compilationnStrong data engineering instincts - comfort designing data pipelines curating training datasets and producing high-quality aggregated datasets at scalenDemonstrated technical leadership: setting architectural direction driving cross-team alignment and mentoring senior engineers