On-Device ML Integration Engineer, Graphics, Games and Machine Learning
Apple
1 day ago
On-site
Cupertino, California, United States
\\nImagine being at the forefront of an evolution where cutting-edge AI meets the elegance of Apple silicon. The On-Device Machine Learning team transforms groundbreaking research into practical applications, enabling billions of Apple devices to run powerful AI models locally, privately, and efficiently. We stand at the unique intersection of research, software engineering, hardware engineering, and product development, making Apple the leading destination for machine learning innovation.\\n\\nOur team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding cutting-edge architectures to embedded systems, developing optimization toolkits for model compression and acceleration, building ML compilers and runtimes for efficient execution, and creating comprehensive benchmarking and debugging toolchains. This infrastructure forms the backbone of Apple’s machine learning workflows across Camera, Siri, Health, Vision, and other core experiences, contributing to the overall Apple Intelligence ecosystem.\\n\\nIf you are passionate about the technical challenges of running sophisticated ML models across all devices, from resource-constrained devices to powerful clusters, and eager to directly impact how machine learning operates across the Apple ecosystem, this role presents an exciting opportunity to work on the next generation of intelligent experiences on Apple platforms.\\n
We are seeking a seasoned ML Engineer with broad experience and an in depth understanding of machine learning, models and infrastructure. In this senior role, you will ensure Apple’s inference stack allows integrating ML workflows end-to-end with excellent user experience, flawless functionality, and maximum performance. This role is far reaching and you will partner with teams across our ML deployment stack, from ML model developers to runtime engineers, as you ensure the best experience, functionality, and maximum performance for ML workflows. The scope of work is wide, spanning model-side updates, ML frameworks export, custom kernels, compiler optimization, and development of analysis and debugging tools.\\n\\nAs a power user of Apple’s ML infrastructure, you will also help spearhead the integration of the latest and most capable models with strong, competitive performance across hardware targets, showcasing the practical power of Apple’s authoring and runtime APIs. This role offers the unique opportunity to shape how ML developers experience Apple’s end-to-end inference stack, from model creation to deployment.
Ensure functional and performant integration of Apple’s ML models across the inference stack.\\n\\nIntegrate Apple’s ML tools into internal and external model repositories to demonstrate and stress-test model ingestion with peak efficiency and performance. \\n\\nDevelop optimizations across the pipeline, including model-level transformations, custom operations, or compiler optimizations to improve inference efficiency.\\n\\nSpearhead the integration of the cutting-edge ML models with peak performance, using these examples to validate or improve Apple’s inference stack.
Bachelors in Computer Sciences, Engineering, or related discipline.\\n\\nProficient in Python programming. Some experience with C++ is required.\\n\\nProficiency in at least one ML authoring framework, such as PyTorch, MLX, and JAX. \\n\\nUnderstanding of ML fundamentals, including common architectures such as Transformers.\\n\\nUnderstanding of GPU programming paradigms.\\n\\nStrong communication skills, including ability to communicate with cross-functional audiences.\\n
Experience with C++, Swift.\\n\\nExperience with GPU kernel optimizations.\\n\\nExperience with MLIR/LLVM or similar compiler toolchains.\\n\\nFamiliarity with Hugging Face or other model repositories.
We are seeking a seasoned ML Engineer with broad experience and an in depth understanding of machine learning, models and infrastructure. In this senior role, you will ensure Apple’s inference stack allows integrating ML workflows end-to-end with excellent user experience, flawless functionality, and maximum performance. This role is far reaching and you will partner with teams across our ML deployment stack, from ML model developers to runtime engineers, as you ensure the best experience, functionality, and maximum performance for ML workflows. The scope of work is wide, spanning model-side updates, ML frameworks export, custom kernels, compiler optimization, and development of analysis and debugging tools.\\n\\nAs a power user of Apple’s ML infrastructure, you will also help spearhead the integration of the latest and most capable models with strong, competitive performance across hardware targets, showcasing the practical power of Apple’s authoring and runtime APIs. This role offers the unique opportunity to shape how ML developers experience Apple’s end-to-end inference stack, from model creation to deployment.
Ensure functional and performant integration of Apple’s ML models across the inference stack.\\n\\nIntegrate Apple’s ML tools into internal and external model repositories to demonstrate and stress-test model ingestion with peak efficiency and performance. \\n\\nDevelop optimizations across the pipeline, including model-level transformations, custom operations, or compiler optimizations to improve inference efficiency.\\n\\nSpearhead the integration of the cutting-edge ML models with peak performance, using these examples to validate or improve Apple’s inference stack.
Bachelors in Computer Sciences, Engineering, or related discipline.\\n\\nProficient in Python programming. Some experience with C++ is required.\\n\\nProficiency in at least one ML authoring framework, such as PyTorch, MLX, and JAX. \\n\\nUnderstanding of ML fundamentals, including common architectures such as Transformers.\\n\\nUnderstanding of GPU programming paradigms.\\n\\nStrong communication skills, including ability to communicate with cross-functional audiences.\\n
Experience with C++, Swift.\\n\\nExperience with GPU kernel optimizations.\\n\\nExperience with MLIR/LLVM or similar compiler toolchains.\\n\\nFamiliarity with Hugging Face or other model repositories.