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  "job_title": "Principal Machine Learning Engineer, AI & Data Platforms (AiDP)",
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  "job_description": "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 Principle 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.\n\nThe 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.\n\nIf you thrive at the intersection of frontier model development, systems engineering, and product creation we want to hear from you.\n\nDescription\nOur Principle 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.\n\nYou 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.\n\",\"responsibilities\":\"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 products\nDesign and implement comprehensive LLM evaluation and benchmarking frameworks, assessing model quality, safety, bias, latency, and cost-efficiency to inform model selection and customisation decisions\nArchitect production inference infrastructure that meets Apple's performance, privacy, and reliability standards at global scale, including model optimisation, quantisation, and efficient serving strategies\nDrive model customisation and adaptation strategies (prompt engineering, retrieval-augmented generation, parameter-efficient and full fine-tuning) to deliver differentiated product experiences\nBuild 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 codebases\nEstablish engineering excellence across the ML development lifecycle, including robust testing, reproducibility, monitoring, documentation, and CI/CD for model and data pipelines\nPartner 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 delivery\nMentor and elevate ML engineers across the team, raising the bar on technical quality and fostering a culture of rigorous experimentation and engineering craft\n\nPreferred Qualifications\nDemonstrated ability to deliver end-to-end AI products - from problem framing and experimentation through to globally deployed, production-grade solutions\nPublished 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 scale\nExpertise in reinforcement learning techniques for model alignment (RLHF, RLAIF, DPO, PPO) and safety/red-teaming methodologies\nDeep familiarity with advanced agentic frameworks and architectures (LangChain, LangGraph, DSPy, AutoGen, or equivalent), including multi-agent orchestration and tool use\nExperience with multimodal AI systems (text, image, code, speech) and cross-modal reasoning\nTrack record of building and shipping standalone AI-native products - not just features - with direct accountability for user impact and product quality\nContributions to open-source ML frameworks, published research, or patents in relevant areas\nExpertise in inference optimisation techniques: quantisation (GPTQ, AWQ), speculative decoding, KV-cache optimisation, and hardware-aware model compilation\nStrong data engineering instincts - comfort designing data pipelines, curating training datasets, and producing high-quality aggregated datasets at scale\nDemonstrated technical leadership: setting architectural direction, driving cross-team alignment, and mentoring senior engineers\n\nMinimum Qualifications\nExtensive hands-on Machine Learning engineering experience, with a demonstrable track record of shipping ML-powered products at scale\nDeep, 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 Apple's technology stack\nExperience 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\n\nAt Apple, we’re not all the same. And that’s our greatest strength. We draw on the differences in who we are, what we’ve experienced and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. As a registered Disability Confident employer, we will work with applicants to make any reasonable accommodations. Apple will consider for employment all qualified applicants with criminal backgrounds in a manner consistent with applicable law. Learn more",
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  "job_posted_at_datetime_utc": "2026-03-17T00:00:00.000Z"
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