Software Engineer, ML platform and Infrastructure

The Applied Machine Learning team has been at the forefront of accelerating digital transformation through machine learning across Apple's enterprise ecosystem. Our ML Platforms, Solutions, and Services deliver a comprehensive suite of capabilities that drive efficiency, agility, and innovation at Apple scale-serving business-critical needs across the enterprise.

We are looking for talented Software Engineers who are passionate about distributed systems and large-scale infrastructure to build and operate world-class ML platforms and products across cloud environments.

Description

Join Apple's Applied Machine Learning Team as a Machine Learning Platform Engineer and play a central role in designing and building the systems that power our Data, Machine Learning, and Generative AI initiatives. You will architect and engineer robust, high-performance, massively scalable platforms that serve as the foundation for groundbreaking ML workloads across the enterprise.

In this role, you will apply software engineering depth to solve the hardest challenges in large-scale distributed systems-designing for reliability, performance, and efficiency from the ground up. You will own the technical direction of ML/Data/Inference platform capabilities, leading the evaluation and integration of cutting-edge open-source technologies and building innovative internal solutions that raise the bar for scalability and resilience across our ML ecosystem. You'll collaborate closely with cross-functional engineering and business teams, influencing technical strategy and contributing meaningfully to the broader platform roadmap.

Minimum Qualifications

5+ years of experience in software development, with a strong focus on backend systems and APIs.

2+ years of experience working with LLMs, Agent Frameworks

5+ years of experience with cloud platforms such as AWS,or Google Cloud Platform

Preferred Qualifications

Experience engineering scalable solutions for data processing and model training/fine-tuning workflows.

Hands-on experience building with distributed data technologies for ML training such as Spark, Flink, Iceberg, or Snowflake, with a deep understanding of their architectural trade-offs at scale.