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

Apple builds AI systems that shape experiences for billions of people, with a commitment to privacy, performance, and craft. Its AI & Data Platforms team develops generative AI systems and products at global scale.

• Lead the end-to-end development and productionisation of LLM-based systems, from upstream training and reinforcement learning through fine-tuning, alignment, and deployment of globally scaled products
• Design and implement LLM evaluation and benchmarking frameworks to assess model quality, safety, bias, latency, and cost-efficiency
• Architect production inference infrastructure, including model optimisation, quantisation, and efficient serving strategies
• Drive model customisation and adaptation strategies, including prompt engineering, retrieval-augmented generation, and parameter-efficient and full fine-tuning
• Build end-to-end AI-powered products and features, owning delivery from problem definition and prototyping through production release across Swift, Java, and Python codebases
• Establish engineering standards across the ML development lifecycle, including testing, reproducibility, monitoring, documentation, and CI/CD for model and data pipelines
• Partner with research, product, design, and platform teams to turn emerging capabilities into scalable, user-centric solutions
• Mentor ML engineers, raise technical quality, and foster rigorous experimentation and engineering craft

• Extensive hands-on Machine Learning engineering experience and a track record of shipping ML-powered products at scale
• Practical expertise in LLM fine-tuning, alignment, and customisation, including RLHF, LoRA, QLoRA, prompt optimisation, and LLM evaluation and benchmarking
• Strong software engineering proficiency in Python, Swift, and Java
• Experience building and operating enterprise-grade ML pipelines in cloud or on-prem environments

Будет плюсом:

• end-to-end AI product delivery, published papers in top ML/Statistics/Maths/computer science conferences, LLM pre-training, reinforcement learning for model alignment, safety and red-teaming, agentic frameworks, multimodal AI systems, standalone AI-native products, open-source contributions, research or patents, inference optimisation, data engineering, architectural direction, cross-team alignment, and mentoring senior engineers