Senior​/Machine Learning Engineer

Senior / Staff Machine Learning Engineer

Cupertino, California, United States Software and Services

Join a team at the forefront of ML infrastructure and generative AI, where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. We focus on building robust systems that connect scalable data pipelines with advanced ML workflows to accelerate the development of real-world AI applications.

The ADP ML Data Platform team enables future Apple intelligent products by providing Apple engineers with cutting edge ML technologies, large scale compute and data systems specifically designed for machine learning. You will build the data foundation that powers ML training across Apple. Our team enables governed, scalable sharing of text and multimodal datasets, ensuring teams can safely discover, access, and use high-quality data for training.

We focus on turning raw data into usable training assets with streamlining data preparation, enabling rapid iteration, and supporting advanced techniques such as synthetic data workflows. Our goal is to remove friction between data creation and model experimentation so teams can move from idea to training quickly and confidently. Most critically, we optimize how data is consumed during training. We work on improving GPU utilization and reducing training bottlenecks through deep benchmarking, profiling, and system‑level optimization of data pipelines.

This includes designing high‑performance data access patterns for large‑scale distributed workloads and ensuring reliability and efficiency  will operate at the intersection of ML systems and infrastructure, partnering with model teams to improve end‑to‑end training performance, eliminate inefficiencies, and raise the bar on reproducibility and governance. We are looking for engineers with strong experience in large‑scale training systems, performance optimization, and data‑intensive ML workloads.

If you care about maximizing efficiency, designing scalable data architectures, and enabling the next generation of generative AI models, this role offers the scope and impact to do exactly that.

• As a member of the Apple ML Data Platform team, your responsibilities will include:
• Design and build the ML data platform that enables governed discovery, sharing, and access to large-scale text and multimodal datasets across the company
• Develop systems that transform raw and synthetic data into high-quality, training‑ready assets, enabling rapid experimentation and iteration
• Architect and optimize high‑performance data pipelines for large-scale distributed training, improving GPU utilization and reducing end‑to‑end training time
• Benchmark and profile training workloads to identify data bottlenecks, and implement system‑level optimizations to eliminate them
• Partner with model and research teams to improve reproducibility, reliability, and performance of training workflows
• Build scalable infrastructure that supports foundation models and next‑generation ML workloads with strong guarantees around lineage, versioning, and compliance

• Strong foundation in machine learning systems, with hands‑on experience in large‑scale training workflows and data‑intensive ML pipelines
• Deep understanding of training performance optimization, including profiling, benchmarking, and eliminating data bottlenecks in distributed environments
• Experience building production‑grade ML data or training infrastructure with strong guarantees around reproducibility, versioning, and governance
• Proven ability to design high‑throughput, low‑latency data pipelines for large‑scale GPU workloads
• Familiarity with modern foundation models and multimodal training workloads
• Experience operating and debugging distributed systems in large‑scale production environments
• Strong systems programming skills in Python and at least one of Java or Go
• Ability to work cross‑functionally with research, infrastructure, and product teams to improve end‑to‑end ML performance
• Comfortable operating in fast‑moving, ambiguous problem spaces with evolving technical requirements
• B.S., M.S., or…