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 \& 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.

Description

Our 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.

","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

Design and implement comprehensive LLM evaluation and benchmarking frameworks, assessing model quality, safety, bias, latency, and cost\-efficiency to inform model selection and customisation decisions

Architect production inference infrastructure that meets Apple's performance, privacy, and reliability standards at global scale, including model optimisation, quantisation, and efficient serving strategies

Drive model customisation and adaptation strategies (prompt engineering, retrieval\-augmented generation, parameter\-efficient and full fine\-tuning) to deliver differentiated product experiences

Build 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

Establish engineering excellence across the ML development lifecycle, including robust testing, reproducibility, monitoring, documentation, and CI/CD for model and data pipelines

Partner 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

Mentor and elevate ML engineers across the team, raising the bar on technical quality and fostering a culture of rigorous experimentation and engineering craft

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

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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