Graphics Research Engineer

Graphics Research Engineer

AMD - Advanced Micro Devices

Munich, Germany

THE ROLE:

AMD is looking for a strategic ML Research engineer who is passionate about creating new experiences with GPU 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 to push the boundaries of what is possible in rendering

THE PERSON:

The ideal candidate should be passionate about Machine Learning, Graphics and software engineering and possess skills to drive innovation and research, able to communicate effectively and work optimally with different teams across AMD. Person will be part of the Advanced Rendering Research team and has the following:

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.
• Proficiency in 3D graphics and Ray tracing using GPU Compute and Graphics APIs like Direct3D, Vulkan, OpenGL, OpenCL, CUDA
• Expertise in either Diffusion or Generative Transformer based LLM models
• Strong proficiency in Python programming for implementing machine learning algorithms, data preprocessing, and model evaluation.
• Comprehensive understanding of general software development workflows, including version control systems like Git, environment management tools like docker, conda, 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.

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.

ACADEMIC CREDENTIALS:

• Bachelors’ or Master's degree in Computer Science, with a focus on areas such as Graphics, Mathematics, Machine Learning, Computer Engineering, or related fields, providing a solid theoretical foundation for advanced machine learning research and development.

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