THE PERSON:

We are looking for a highly motivated and skilled Principal Machine Learning Software Engineer to join our team. You will work with a team of Software Engineers to enable models, libraries, and applications for Instinct GPUs in both on-prem and Cloud environments. Candidates should have strong Python or C++ Programming experience working with LLM’s (Large Language Models) Candidates should also have experience analyzing and optimizing the performance of software. Must be self-motivated and possess the ability to work well within a team environment. This is a senior level role, you must have at least 4 years of post college experience.

JOB DESCRIPTION

As a Machine Learning Engineer specializing in low-level performance optimization, you will play a critical role in helping our customers to advance AMD-based machine learning infrastructure and ensuring the efficient deployment of state-of-the-art large models. You will be part of a dynamic team working on groundbreaking projects and will be responsible for optimizing model execution, including GPU kernels, both for inference and training, in a multi-GPU and multi-node environment. Your contributions will directly impact our ability to deliver cutting-edge AI solutions efficiently and at scale.

PREFERRED QUALIFICATIONS

• A Bachelor, Master’s or Ph.D. in Computer Science, Electrical Engineering, or a related field or equivalent practical experience.
• Solid understanding of GPU accelerators like ONNX, DeepSpeed, VLLM
• Strong experience in low-level GPU kernel optimization.
• Proficiency in CUDA and GPU programming.
• Experience with distributed computing and multi-GPU environments.
• Proficiency in performance profiling and optimization tools.
• Solid programming skills in Python
• Experience with deep learning frameworks (e.g., JAX, PyTorch, TensorFlow).
• Excellent problem-solving skills and attention to detail.
• Strong communication and teamwork skills.
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WHAT YOU DO AT AMD CHANGES EVERYTHING

We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.
AMD together we advance_
Responsibilities:

THE ROLE:

Would you like to be part of a world class team enabling software for world class datacenters and the mightiest supercomputers? AMD is searching for talented and highly motivated Software Engineers to join our team of developers pushing the boundaries of efficiency and performance to enable and optimize the software ecosystem for the next generation of GPU computational accelerators. Our team has an unparalleled perspective of the AI landscape. We work with the industry’s most sophisticated clients to help them leverage the latest hardware capabilities for their AI use cases. As part of our team, you will be among the first in the world to combine the newest hardware with the industry’s latest applications, libraries, frameworks, and SDKs to push the limits of innovation and solve the world’s most complex challenges.

KEY RESPONSIBILITIES

• GPU Kernel Optimization: Develop and optimize low-level GPU kernels to accelerate inference and training of large machine learning models. Maximize the computational efficiency and reduce execution time while ensuring model accuracy. Expert knowledge and hands-on experience in C, C++.
• Multi-GPU and Multi-Node Optimization: Design and implement strategies for distributed model training and inference across multiple GPUs and nodes. Address data parallelism and model parallelism challenges to fully utilize available resources.
• Performance Profiling: Profile and analyze system and application performance to identify bottlenecks and areas for improvement. Use profiling tools to understand and optimize hardware resource utilization.
• Parallel Computing: Leverage parallel computing techniques to improve the scalability and performance of machine learning workloads. Implement multi-threading and GPU synchronization techniques.
• Model Quantization: Explore and apply model quantization techniques to reduce memory and computation overhead, especially for edge and cloud deployment.
• Benchmarking and Testing: Develop benchmarks and testing procedures to assess the performance and stability of optimized models and frameworks. Ensure that the solutions meet or exceed the defined performance criteria.
• Collaboration: Collaborate closely with machine learning researchers, software engineers, and infrastructure teams to integrate optimized kernels and solutions into production systems.
• Documentation: Create detailed documentation of optimizations, best practices, and implementation guidelines to facilitate knowledge sharing and maintainable code.