ML Software Engineer

Our team builds ML-inference applications and services on Apple Silicon in the datacenter, specifically focusing in recent years on generative AI as part of the Private Cloud Compute component of Apple Intelligence.

Description

As part of the team you will help engineer continuous improvements in stability and performance for private cloud compute, as well as help implement entirely new functionality as it emerges from the research community, in collaboration with product teams throughout Apple.

We write performant and scalable frameworks (in Swift and C++) to distribute and coordinate ML inference tasks to different hardware acceleration IP blocks on different SoCs.

We're a collection of highly skilled and friendly engineers who value each other's opinions and experience. We strive for excellence and believe strongly in the quality of our output. We have formed a team of domain experts who specializes in specific core subject areas, and also have broad experience of cloud software services and platforms.

You will integrate inference code into a full service stack to ensure that user traffic is served reliably and performantly, and will have a strong focus on developing code that is easy and safe to develop, update and monitor.

Preferred Qualifications

Bonus qualification: familiar with Apple ML stack (ANE, CoreML, MPS/Metal), high-level general distributed ML stack (PyTorch-distributed, NCCL) and high throughput inter-chip communication systems.

Bonus qualification: on-device iOS development

ADDITIONAL REQUIREMENTS

Quality focus - produce reliable, maintainable, deliverable software - Comfortable diving deep - working across multiple levels of abstraction - Good at handling relationships & communication - collaborate well with colleagues across a wide range of functions

Minimum Qualifications

Experience working as a software engineer on large production systems

Experience programming in: Swift, C, C++, iOS/macOS or XCode

Practical experience running machine learning models and evaluating them for quality and performance metrics

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