mid machine learning ML Platform Engineer ic · Posted Aug 14, 2025
$193,300 – $261,500
USD per year

About this role

Amazon is hiring a mid-level ML Platform Engineer in the machine learning function based in Cupertino, CA. The posting calls out experience with CUDA, AWS, TensorFlow, PyTorch. Compensation is listed at $193,300–$261,500 per year.

Role
ML Platform Engineer
Function
machine learning
Level
mid
Track
Individual contributor
Employment
Full-time
Location
Cupertino, CA
Department
Software Development
Posted
Aug 14, 2025
AI Summary
Mid-level ML Platform Engineer optimizing high-performance kernels for AWS ML accelerators at the hardware-software boundary. Develops compiler, runtime, and framework components for deep learning workloads. Requires expertise in low-level optimization, system architecture, and ML acceleration with strong C++ and hardware knowledge.

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Job description

from Amazon careers

The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon’s custom machine learning accelerators, Inferentia and Trainium. The Acceleration Kernel Library team is at the forefront of maximizing performance for AWS's custom ML accelerators. Working at the hardware-software boundary, our engineers craft high-performance kernels for ML functions, ensuring every FLOP counts in delivering optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch, enabling unparalleled ML inference and training performance. As part of the broader Neuron Compiler organization, our team works across multiple technology layers - from frameworks and compilers to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This…

This is an excerpt. Read the full job description on Amazon careers →
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