Machine Learning Engineer

Summary

Video is at the core of nearly all Apple products, and as a research engineer on our team, you will build the infrastructure, pipelines, systems, and models that power the next generation of video technology. We are looking for a highly ambitious individual, who will flourish working on technically meaningful problems across the full ML lifecycle — including training infrastructure, performance optimization, data, and production deployment. Your work will redefine the video experience for billions of users.

Minimum Qualifications

BS and 10+ years of hands-on industry experience building production ML systems, with a proven track record of design/implementation leadership Deep familiarity with in of ML-centric flows and best practices: training procedures, dataloaders, profiling and debugging methodology, data handling, model development, model deployment Proficiency in Python, C++, PyTorch Familiarity with computer architecture principles Proficiency in AI tooling such as Claude and excellent high-level design skills — allowing building robust, modular, clean, and well-tested code

Preferred Qualifications

MS specializing in ML systems, performance engineering, or a related area Experience implementing custom ops in CUDA or low-level GPU kernel optimization Research experience in ML model design Experience with visual data (e.g. images/videos/3DGS) and related vision algorithms Experience with distributed training, large-scale data pipelines, inference serving Excellent written and oral communication skills

Description

In this role you will work together with colleagues to design, scale, and harden the systems that bring ML-based video approaches into current and future Apple products. This position requires a highly self-directed individual, who is comfortable working at the intersection of ML, systems engineering, and performance optimization. Strong engineering and analytical skills will be critical towards solving challenging problems across efficiency optimization, training, data, and deployment.

Responsibilities

Design, build, test, and scale ML systems: training infrastructure, data ingestion and curation pipelines, power and performance optimization for inference, ML model development Collaborate closely with team members and cross-functional teams to achieve quality, throughput, computational requirements, and robustness towards shipping the technology Communicate: demonstrate the technology, present to leadership, share progress with colleagues, and so on