Director Machine Learning Engineering – AI/ML Model Compiler and Applications

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WHAT YOU DO AT AMD CHANGES EVERYTHING

At AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges-striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.

THE ROLE:

We are looking for a Director, Machine Learning to join our AI/ML Model Compiler and Applications team. If you are excited by the challenge of distributed training of large models on Neural Processing Unit (NPUs), and if you are passionate about improving training efficiency while innovating and generating new ideas, then this role is for you. You will be part of a world class team focused on addressing the challenge of training generative AI.

THE PERSON:

The ideal candidate should have experience with delivering ML compiler solutions, understanding of NPU hardware architecture, defining scalable algorithms that enables efficient problem mapping, interfacing with customers to understand the requirements and driving a team to develop solutions.

KEY RESPONSIBILITIES:

• Lead a growing team focused on research, design, and implement novel methods for efficient Generative AI.
• Experience in developing ML compilers for low power inference architecture
• Experience in all 3 stages of ML compiler- front end, middle end and backend
• Experience in defining optimized tiling algorithms, fusion algorithms at different memory hierarchy
• Influence the direction of AMD AI platform.
• Collaborate across teams with various groups and stakeholders.
• Propose and apply innovative model quantization, sparsity, and acceleration algorithms on various AMD platform, e.g., GPU/CPU/AIE.
• Collaborate with software and hardware team to E2E co-optimize model performance.
• Work with open-source framework and community (e.g., Pytorch, Huggingface) to integrate AMD optimized models and libraries.

PREFERRED EXPERIENCE:

• Experience with ML frameworks such as Onnx, PyTorch, or TensorFlow.
• Experience with distributed training and distributed training frameworks, such as DeepSpeed.
• Experience with LLMs or computer vision, especially large models, is a plus.
• Excellent C++ and Python programming skills, including debugging, profiling, and performance analysis.
• Experience with ML pipelines.
• Strong communication and problem-solving skills.
• Strong leadership and management skills.

ACADEMIC CREDENTIALS:

• A PhD or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

Benefits offered are described: AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies or fee based recruitment services. AMD and its subsidiaries are equal opportunity employers. We consider candidates regardless of age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status.

 

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