Senior Machine Learning Engineer

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.

Responsibilities

As a member of the Apple Cloud AI Platform team, your responsibilities will include:

Design and build the platform behind Apple’s largest model builds - ingestion, immutable versioning, lineage, and governance across structured, unstructured, and multimodal data at petabyte scale, so every model run is reproducible from a versioned dataset

Develop and evolve Python SDKs and core data libraries that ML engineers depend on to access, transform, and load model-ready datasets across every stage of model development

Build high-throughput data access and loading primitives that feed Apple’s largest GPU fleets, keeping workloads compute-bound rather than I/O-bound

Build and operate distributed data pipelines spanning Spark, Daft, and Rust-based systems for ingestion, transformation, and large-scale data preparation

Optimize platform components for tight integration with leading ML frameworks - PyTorch, JAX, and TensorFlow - so dataset access is a first-class concern in the model development loop

Partner with research and product teams to onboard new data sources, and enable rapid iteration on datasets powering GenAI workloads

Ensure governance is a first-class platform capability: Legal Terms of Use enforcement, privacy controls, and end-to-end data lineage on every dataset version

Drive efficiency, reliability, and automation across the data plane and control plane that power Apple’s ML fleet

Continuously evolve platform capabilities to support next-generation workloads, including foundation models, multimodal data, and retrieval-augmented systems

Diagnose, fix, and automate away complex issues across the stack - from ingestion pipelines to dataset APIs to ML framework integrations - to maximize uptime and throughput

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)

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $175,000 and $308,500, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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.