Responsibilities
- As an AMD co-op
- , you’ll be placed at the epicenter of the AI ecosystem, working alongside experts and industry pioneers. You’ll do important work, learn new skills, expand your network, and gain real-world experience on projects that impact millions of end-users worldwide. Whether you’re an undergrad or a PhD student, your contributions matter—and your experience here will be a launchpad for what comes next.
JOB DETAILS:
Location:
San Jose, CA, USA
Onsite/Hybrid:
This role requires the student to work full time (40 hours a week), either in a hybrid or onsite or remote work structure throughout the duration of the co-op term.
Duration:
Spring/Summer 2027 Co-Op: January 25, 2027 - August 13, 2027
WHAT YOU WILL BE DOING:
We are seeking a highly motivated Agentic/ML System Co-op who is pursuing a master’s or PhD degree in Computer Science, Electrical Engineering, or a related field and has a strong interest in machine learning systems and agentic AI. In this role, you will collaborate with our research and engineering teams to explore techniques that improve the efficiency, scalability, and performance of open-weight large language models on modern hardware architectures.
Work with state-of-the-art open-weight large language models (LLMs) and leverage frontier coding agents to accelerate research and development.
Contribute to agentic system design and software and hardware optimizations for machine learning workloads across AMD GPUs, vLLM, SGLang, and PyTorch.
Collaborate with ML scientists, engineers, and architects in a dynamic, fast-paced environment that reflects the rapidly evolving AI ecosystem.
Document your findings and contribute to technical reports, presentations, and potentially research publications.
WHO WE ARE LOOKING FOR:
Currently enrolled in a master’s or PhD program in Computer Science, Electrical Engineering, or a related field at a U.S.-based university; PhD candidates are preferred.
Proficiency in Python and/or C++, with exposure to performance profiling or optimization.
A general understanding of machine learning software stacks and large language models, including frameworks such as PyTorch or JAX and coding-agent harnesses.
A builder’s mindset, strong motivation, and intellectual curiosity, along with the ability to explore unfamiliar areas and incorporate feedback effectively.
Demonstrated knowledge or experience in at least one of the following areas, preferably through relevant projects:
Agentic software engineering, agent-harness development and evolution, or agent and skill evaluation.
Performance analysis, GPU architecture, GPU kernel development and debugging, or machine learning compilers.
Note:
This role is not eligible for visa sponsorship.
Qualifications
