Neural Rendering Research Inference Engineer - Advanced Graphics Programs

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We are seeking an exceptional Neural Rendering Research Inference Engineer – Advanced Graphics Program who has deep technical expertise in translating neural network models and algorithms to efficient inference solutions. The ideal candidate should be passionate about the intersection of Graphics and machine learning with a profound understanding of common neural network operators, their mathematical foundations and computational requirements, coupled with strong programming expertise in C++, HIP, CUDA, HLSL and GPU‑related technologies.

The individual should be able to effectively communicate and work optimally with different teams across AMD to create optimized inference implementations for neural rendering and generative AI applications.

AMD is looking for a strategic research inference engineer who is passionate about improving the performance of key ML applications by designing, implementing and optimizing high performance GPU kernels for ML operators. You will be a member of a core team working with incredibly talented ML researchers who are path‑finding new technologies in real‑time neural graphics, inverse rendering and other ML accelerated opportunities.

• Work with ML researchers and engineers to translate neural network models and algorithms written in PyTorch/Onnx to efficient GPU shaders using languages such as HIP, CUDA, HLSL.
• Design, implement and optimize high‑performance GPU kernels for ML operators.
• Work across research, hardware, driver and compiler teams to analyze and troubleshoot performance issues, provide solutions to improve rendering speed and ML workload efficiency.
• Stay current with latest advancements in GPU hardware, rendering techniques, graphics APIs and GPU accelerated ML.
• Contribute to the design and development of tools and methodologies for optimized shader integration to game engines.
• Document and share knowledge on best practices for GPU programming (both graphics and compute/ML) within the team.
• Participate in code reviews and provide constructive feedback to peers.

• Strong object‑oriented programming background, C/C++ preferred.
• Proven experience in developing and optimizing GPU kernels for machine learning workloads (e.g., using HIP, OpenCL, CUDA, HLSL).
• Ability to program in low‑level languages (x86 asm, SSE, ISA, PTX, AMD assembly).
• Strong understanding of GPU architecture (compute cores, cache hierarchy, memory model), graphics APIs (Direct
  
  X, OpenGL, Vulkan, etc.), and shader programming.
• Solid understanding of common neural network operators, their mathematical foundations, and computational requirements.
• Experience with modern concurrent programming and threading APIs.
• Experience with Windows, Linux operating system development.
• Experience with software development processes and tools such as debuggers, source code control systems (Git Hub) and profilers is a plus.
• Understanding of machine learning techniques and its application within graphics.
• Effective communication and problem‑solving skills.
• Good interpersonal skills.

• Undergraduate degree required. Master’s degree or Ph.D. in Computer Science especially Graphics and/or Machine Learning, Computer Engineering, or equivalent preferred.

This role is not eligible for Visa sponsorship.

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.