Senior Machine Learning Engineer

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 build robust systems that connect

scalable data pipelines with advanced ML workflows, accelerating the development

of real-world AI applications. Our work spans the full ML lifecycle, from

experimentation to deployment, and you’ll play a key role in shaping how AI

models are built, optimized, and scaled. We develop a platform for ML data and

features that powers advanced GenAI applications. This includes embeddings

(generation, evaluation, ANN search, multimodal support), AI Ops, efficient

inference, and a modern feature platform designed to streamline experimentation

and drive innovation. We’re looking for engineers and researchers passionate

about generative models, data-centric ML, and intelligent systems across diverse

real-world use cases. With the autonomy to experiment, the scale to make an

impact, and the support to take ideas from prototype to production, you’ll work

alongside a world-class team to build intelligent, flexible systems that make ML

development faster, more reliable, and more creative.

DESCRIPTION

The Apple Cloud AI Platform team enables Apple's next generation of intelligent

products by giving Apple's ML engineers and researchers the data systems and

large-scale compute they need to build and ship models at Apple's bar for

quality and privacy.

MINIMUM QUALIFICATIONS

Strong foundation in machine learning, with hands-on experience across the

end-to-end ML workflow - including data preparation, pipeline development,

experimentation, evaluation, and deployment Expertise in building and running

large scale distributed systems Familiarity with modern generative techniques

(e.g. transformers, diffusion, retrieval-augmented generation) Proven experience

building and delivering data and machine learning infrastructure in real-world

production environments Familiarity with fine-tuning workflows, model

optimization, and preparing models for scalable inference Familiarity with

generative AI and its applications in accelerating and enhancing machine

learning workflows Experience configuring, deploying and troubleshooting large

scale production environments Experience in designing, building, and maintaining

scalable, highly available systems that prioritize ease of use Extensive

programming experience in Java, Python or Go Strong collaboration and

communication (verbal and written) skills Comfortable navigating ambiguity and

evolving technical landscapes, especially in fast-moving areas B.S., M.S., or

Ph.D. in Computer Science, Computer Engineering, or equivalent practical

experience

PREFERRED QUALIFICATIONS

Experience in any of the below is preferred: Proficiency with one or more modern

ML frameworks (PyTorch, JAX, or TensorFlow), particularly the data loading and

dataset access layer Columnar and lakehouse formats: Parquet, Iceberg, Delta, or

Lance Distributed data loading frameworks for ML: Ray Data, NVIDIA DALI,

WebDataset, or Mosaic StreamingDataset Performance engineering for I/O-bound

workloads — Arrow, zero-copy, memory mapping, async I/O High-throughput object

storage access patterns at GPU scale Data lineage and governance systems

(DataHub, OpenLineage, Unity Catalog, or equivalent) Contributions to or

operational experience with Spark, Daft, Polars, or DuckDB internals

Containerization and orchestration technologies (Docker, Kubernetes)