Senior GenAI Platform Engineer - ML Infra

Apple Inc.; Seattle (WA); On-site; USD 175,000 - 309,000; Full time

Benefits offered by this job

Medical and dental coverage

Retirement benefits

Discounted products and services

Educational reimbursement

Bonuses or commission payments

Relocation assistance

Job summary

Apple Inc. is seeking engineers and researchers to join the Apple Cloud AI Platform to design and maintain data systems and large-scale compute for model development and GenAI workloads.

You will build an end-to-end ML data platform, collaborating with ML engineers and researchers to ingest, transform, and deploy datasets at scale, while ensuring governance, privacy, and reliability across the ML lifecycle.

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

Responsibilities

• 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

Skills

Distributed systems

Java

Python

Go

Machine learning

Data pipelines

PyTorch

TensorFlow

Communication

Education

B.S./M.S./Ph.D. in CS/CE

Tools

Spark

Daft

Rust

Docker

Kubernetes

Polars

DuckDB

Job description

Apple Inc. is seeking engineers and researchers to join the Apple Cloud AI Platform to design and maintain data systems and large-scale compute for model development and GenAI workloads.

You will build an end-to-end ML data platform, collaborating with ML engineers and researchers to ingest, transform, and deploy datasets at scale, while ensuring governance, privacy, and reliability across the ML lifecycle.