ML Architect Engineer

at  Advanced Micro Devices Inc

PREFERRED EXPERIENCE:

• Extensive knowledge and hands-on experience in machine learning, with a track record of successfully creating and optimizing machine learning models for various application especially around ML Model Architectures.
• Demonstrated expertise in designing efficient and scalable model architectures tailored to specific problem domains or computational resources.
• Familiarity with 3D graphics and ray tracing techniques using GPU Compute and popular graphics APIs such as Direct3D, Vulkan, OpenGL, OpenCL, CUDA, and HIP.
• Ability to write high-quality, maintainable code with meticulous attention to detail, ensuring robustness and performance optimization.
• Experience with modern concurrent programming paradigms and threading APIs to develop parallel and distributed machine learning algorithms efficiently.
• Proficiency in both Windows and Linux operating system development environments, including experience with system-level programming and optimization.
• Familiarity with software development processes and tools such as debuggers, source code control systems (e.g., GitHub), and performance profilers, providing insights into code behavior and performance bottlenecks.
• Strong programming skills in C++ for implementing performance-critical components of machine learning frameworks or applications.
• Effective communication and problem-solving skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.
• Demonstrated leadership qualities and interpersonal skills, capable of motivating and guiding team members to achieve project goals effectively.

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:

AMD is looking for a strategic ML architect engineering leader who is passionate about creating new algorithms with GPUs on image processing, rendering. You will be a member of a core team of incredibly talented industry specialists and will work with the very latest hardware and software technology.
More about the ARR team: Advanced Rendering Research Group - AMD GPUOpen

KEY RESPONSIBILITIES:

• Expertise in Machine Learning, particularly focused on Model Creation and Model Architecture, including advanced techniques such as deep learning, reinforcement learning, and generative models.
• Strong proficiency in Python programming for implementing machine learning algorithms, data preprocessing, and model evaluation.
• Ability to work with D3D12
• Comprehensive understanding of general software development workflows, including version control systems like Git, build automation tools like CMake, and continuous integration (CI) pipelines.
• Proficient in English, with excellent written and verbal communication skills for collaborating with team members and presenting findings or proposals.
• Collaborate with cross-functional teams including data scientists, engineers, and domain experts to understand requirements, develop prototypes, and deploy production-ready machine learning solutions.
• Research and stay up-to-date with the latest advancements in machine learning algorithms, frameworks, and tools, incorporating best practices into model development and architecture design.
• Optimize machine learning models for deployment on various platforms including cloud infrastructure, edge devices, and embedded systems, balancing performance, resource constraints, and scalability requirements.
• Conduct thorough experiments and evaluations to assess model performance, reliability, and robustness, employing techniques such as hyperparameter tuning, cross-validation, and A/B testing.
• Document code, methodologies, and findings comprehensively, ensuring reproducibility and knowledge sharing within the team and across the organization.
• Mentor junior team members, providing guidance on machine learning concepts, programming techniques, and software development practices to foster skill development and team growth.