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# Machine Learning Engineer

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Salary:** $184,700.0
- **Contract:** Permanent contract
- **Skills:** Apple Products, Systems Engineering, C++ (Programming Language), Profiling, Nvidia CUDA, Software Debugging, Distributed Computing Environment, High-Level Architecture, Python (Programming Language), Machine Learning, Performance Tuning, Data Processing, Data Ingestion, Pytorch, Machine Learning Operations, Data Pipelines
- **Published:** September 3, 2026
- **Apply:** https://www.themuse.com/jobs/apple/machine-learning-engineer-d4653f

## About the Role

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, 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

## 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


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