Senior Machine Learning Engineer

Apple, Inc.; Seattle (WA); Sur place; USD 175 000 - 309 000; Plein temps

Avantages offerts par ce poste

Apple Benefits

Relocation

Stock options

Résumé du poste

Apple's Seattle AI Platform team builds data systems and large‑scale compute to train and ship models for Apple products and services. You will own major components of the data layer, enabling reproducible model runs and scalable ML data pipelines.

You will work across PyTorch/JAX/TF integrations, contribute to high‑throughput data ingestion, and collaborate with researchers to onboard new data sources while maintaining robust, production‑grade systems.

Qualifications

• 5+ years of work experience in ML infrastructure, distributed data systems, or a related field.
• 5+ years of experience building and shipping data or ML infrastructure in production.
• Experience operating production systems: monitoring, observability, on‑call, and incident response.
• Strong programming skills in Python, plus a systems language (Rust preferred; C++ or Go acceptable).
• Familiarity with columnar and lakehouse formats (Parquet, Iceberg, Delta, Lance).
• Hands‑on performance engineering for I/O‑bound workloads: Arrow, zero‑copy, memory mapping, async I/O, high‑throughput object storage access patterns.
• Knowledge of end‑to‑end ML workflows and model training/inference data needs.
• Familiarity with modern ML/generative techniques (transformers, diffusion, RAG) for design purposes.
• Experience designing scalable, highly available data systems with good usability for engineers.
• Strong collaboration and written/verbal communication skills.
• BS/MS/PhD in Computer Science/Engineering or equivalent experience.

Responsabilités

• Build and own high‑throughput data access and loading primitives for Apple's large GPU/TPU fleets.
• Develop Python and Rust data libraries and SDKs for data access, transformation, and loading of model‑ready datasets.
• Create and operate distributed data pipelines across Spark, Daft, and Rust‑based systems for ingestion and large‑scale data prep.
• Contribute to platform components for ingestion, versioning, lineage, and governance across structured and unstructured data at petabyte scale.
• Integrate dataset access with PyTorch, JAX, and TensorFlow data loading layers in model development.
• Onboard new data sources with research and product teams to support GenAI workloads.
• Diagnose, fix, and automate issues across ingestion to APIs and framework integrations to maximize uptime.

Connaissances

Python

Rust

C++/Go

PyTorch

JAX

TensorFlow

Docker

Kubernetes

Spark

Daft

Polars

Formation

BS/MS/PhD in Computer Science or related field

Outils

Parquet

Iceberg

Delta

Lance

Description du poste

Seattle, Washington, 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 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 AI Platform team gives 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. Our team owns the data layer that large‑scale model training depends on: ingestion, versioning, lineage, and governance on the way in, and high‑throughput data loading into the training fleet on the way out. As a Senior ML Infrastructure Engineer, you will own and ship major components of that platform.

Responsibilities

• Build and own high‑throughput data access and loading primitives that feed Apple's largest GPU and TPU fleets, keeping training compute‑bound rather than I/O‑bound.
• Develop and evolve the Python and Rust data libraries and SDKs that ML engineers depend on to access, transform, and load model‑ready datasets across every stage of model development.
• Build and operate distributed data pipelines across Spark, Daft, and Rust‑based systems for ingestion, transformation, and large‑scale data preparation.
• Contribute to 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.
• Make dataset access a first‑class concern in the model development loop through tight integration with the data‑loading layers of PyTorch, JAX, and TensorFlow.
• Partner with research and product teams to onboard new data sources and unblock fast iteration on the datasets powering foundation‑model and GenAI workloads.
• Diagnose, fix, and automate away complex issues across the stack, from ingestion pipelines to dataset APIs to framework integrations, to maximize uptime and throughput.

Minimum Qualifications

• * 5+ years of work experience in machine learning infrastructure, distributed data systems, or a related field.
• * 5+ years of experience building and shipping data or ML infrastructure and systems in production.
• * Experience operating production systems: monitoring, observability, on‑call, and incident response.
• * Strong programming skills in Python, plus a systems language for performance‑critical work (Rust strongly preferred; C++ or Go acceptable).
• * Familiarity with columnar and lakehouse formats (Parquet, Iceberg, Delta, or Lance) and the trade‑offs between them.
• * Hands‑on performance engineering for I/O‑bound workloads: Arrow, zero‑copy, memory mapping, async I/O, and high‑throughput object storage access patterns.
• * Working knowledge of the end‑to‑end ML workflow and how training and inference workloads consume data, enough to design data systems that serve them well.
• * Familiarity with modern ML and generative techniques (transformers, diffusion, retrieval‑augmented generation, fine‑tuning) at the level needed to design for those consumers, not necessarily to build the models yourself.
• * Experience designing and operating scalable, highly available systems that prioritize ease of use for the engineers who depend on them.
• * Strong collaboration and written and verbal communication skills.
• * B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience.

Preferred Qualifications

• * Deep experience with the data‑loading and dataset‑access layer of a modern ML framework (PyTorch, JAX, or TensorFlow).
• * Distributed data‑loading frameworks for ML: Ray Data, NVIDIA DALI, WebDataset, or Mosaic StreamingDataset.
• * Experience feeding data to GPU or TPU fleets at scale and keeping them saturated.
• * 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 (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.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

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Apple accepts applications to this posting on an ongoing basis.