Apple
Principle Machine Learning Engineer, AI & Data Platforms (AiDP)
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Principle 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 & 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. 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.
DESCRIPTION
Our 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. 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.
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MINIMUM QUALIFICATIONS
Extensive hands-on Machine Learning engineering experience, with a demonstrable track record of shipping ML-powered products at scale Deep, 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 stack Experience 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
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PREFERRED QUALIFICATIONS
Demonstrated ability to deliver end-to-end AI products - from problem framing and experimentation through to globally deployed, production-grade solutions Published papers in top conferences in ML/Statistics/Maths/compsci. Experience with pre-training or continued pre-training of large language models, including data curation, curriculum design, and training stability at scale Expertise in reinforcement learning techniques for model alignment (RLHF, RLAIF, DPO, PPO) and safety/red-teaming methodologies Deep familiarity with advanced agentic frameworks and architectures (LangChain, LangGraph, DSPy, AutoGen, or equivalent), including multi-agent orchestration and tool use Experience with multimodal AI systems (text, image, code, speech) and cross-modal reasoning Track record of building and shipping standalone AI-native products - not just features - with direct accountability for user impact and product quality Contributions to open-source ML frameworks, published research, or patents in relevant areas Expertise in inference optimisation techniques: quantisation (GPTQ, AWQ), speculative decoding, KV-cache optimisation, and hardware-aware model compilation Strong data engineering instincts - comfort designing data pipelines, curating training datasets, and producing high-quality aggregated datasets at scale Demonstrated technical leadership: setting architectural direction, driving cross-team alignment, and mentoring senior engineers
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