GPU ML Engineer

Key Skills

Computer Vision (CV), GPUs, Image processing, JAX framework, Linear Algebra, Machine learning techniques, Python Programming, PyTorch, TensorFlow

Job Description

Responsibilities:

Adding optimized GPU compute kernels across Machine Learning, Image Processing, Linear Algebra and Computer Vision.

Defining and implementing APIs in Metal Performance Shaders.

Performing in-depth analysis, compiler and kernel level optimizations to ensure the best possible performance across hardware families.

Partnering with Platform Architecture teams to define Apple GPU's compute hardware roadmap. Working with hardware team to analyze performance on future silicon.

Develop/maintain/optimize ML Inference and Training acceleration technologies.

Intended deliverables:

GPU compute acceleration technology.

Optimized compute kernels, computational graph and ML training / inference technologies across products.

If this sounds of interest, we would love to hear from you!

Minimum Qualifications

• Proven programming and problem-solving skills.
• Good understanding of machine learning fundamentals.
• GPU compute programming models & optimization techniques.
• GPU compute framework development, maintenance, and optimization.
• Machine learning development using one or more ML frameworks (TensorFlow, PyTorch or JAX).

Preferred Qualifications

• Experience with adding computational graph support, runtime or device backend to Machine learning libraries (TensorFlow, PyTorch or JAX) support is a plus.
• Experience with high performance parallel programming, GPU programming or LLVM/MLIR compiler infrastructure is a plus.
• Experience with system level programming and computer architecture.
• Background in mathematics, including linear algebra and numerical methods.