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THE ROLEAMD is looking for a research engineer with practical implementation experience to advance AI-based medical imaging at the intersection of clinical imaging, deep learning, and high-performance computing on AMD platforms.
You will work across the full medical imaging workflow, from data preparation and preprocessing through model inference, post-processing, and evaluation, investigating modern pipelines and optimizing their design and implementation on AMD platforms. You will build reference designs, GPU kernels, and agentic frameworks that let these pipelines run at maximum performance on AMD devices such as the Ryzen AI Max+ and Radeon AI Pro, with hardware-model co-design as a central theme: tailoring model architecture to the properties of the underlying hardware so that computation maps efficiently onto the device. Areas of interest include image reconstruction, segmentation, detection, classification, tracking, and quality assessment across modalities such as ultrasound, CT, MRI, and X-ray.
Working with AMD engineering and research teams, industry partners, and clinical collaborators, you will take imaging pipelines from problem definition through implementation, optimization, and evaluation; document your methods and results; and feed what you learn back to the libraries and kernel teams. Your findings will help establish the design frameworks and best practices for customers migrating imaging pipelines to AMD devices, and over time steer the team's roadmap toward the co-design of next-generation imaging models: architected from the outset around AMD hardware for better mapping and efficiency.
THE PERSONYou bring a strong understanding of AI-based medical imaging and the programming skills to turn imaging models and algorithms into efficient software on AMD hardware. You have demonstrated experience writing code, profiling and tuning performance, and solving difficult technical problems. You can distinguish improvements that matter to a clinical imaging workflow running on device from gains in an isolated benchmark and communicate your findings clearly to both domain experts and systems engineers.
We value depth in one or more relevant areas, curiosity across the clinical, algorithmic, and systems disciplines, and the ability to take ownership of a technical project from investigation to validated results.
KEY RESPONSIBILITIES- Analyze modern AI-based medical imaging pipelines, understanding their clinical objectives, algorithms, computational requirements, and practical limitations.
- Collaborate with research and industry partners to design and build new AI models for medical imaging.
- Build or modify components of medical imaging pipelines across modalities such as ultrasound, CT, MRI, and X-ray.
- Prepare, preprocess, and validate imaging data in coordination with the team's data-handling and privacy procedures.
- Integrate and evaluate AI models for tasks such as reconstruction, segmentation, detection, classification, and tracking.
- Measure and profile model and pipeline performance against agreed metrics such as latency, throughput, occupancy, and memory use.
- Develop agentic frameworks that let domain experts target AMD devices more efficiently at the algorithm and graph-design level.
- Develop prototypes, scripts, tests, and technical documentation.
- Present findings and recommendations to internal teams, collaborators, and partners.
REQUIRED EXPERIENCE- Strong programming skills in Python and experience with C++ or another language used for high-performance computing.
- Hands-on experience implementing machine learning or deep learning models for imaging applications.
- Experience with agentic design flows and AI coding tools such as Claude.
- Solid understanding of image processing and common medical imaging concepts.
- Experience profiling, debugging, and improving computational software.
- Ability to make progress independently while on a defined technical problem and collaborate effectively across medical and engineering disciplines.
- Clear written and verbal communication skills.
PREFERRED EXPERIENCE- Medical imaging modalities such as ultrasound, CT, MRI, or X-ray, in depth.
- Imaging libraries and formats such as SimpleITK, ITK, MONAI, OpenCV, DICOM, and NIfTI.
- Deep learning frameworks such as PyTorch or TensorFlow, including custom operators or distributed training and inference.
- Inference deployment and optimization with ONNX Runtime, TensorRT, ROCm, MIGraphX, HIP, CUDA, Triton, or comparable stacks.
- Real-time, embedded, or GPU-accelerated pipelines.
- GPU profiling and tuning of AI models to target GPU hardware more effectively.
- Clinical or regulated data workflows, demonstrated through research, coursework, or projects.
ACADEMIC CREDENTIALSMaster's or PhD in Biomedical Engineering, Computer Science, Computer Engineering, Electrical Engineering, Medical Physics, or a related discipline, or equivalent practical experience preferred.
LOCATION: Longmont, CO; Austin, TX and Santa Clara, Ca are options.
This role is not eligible for visa sponsorship.#LI-MR1
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 may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
This posting is for an existing vacancy.
